Python Tutorial For Beginners – A complete guide

Reading Time: 8 minutes

Python is a high-level, object-oriented programming language. Most beginners in the development field prefer Python as one of the first languages to learn because of its simplicity and versatility. It is also well supported by the community and keeps up with its increasing popularity. In this Python Tutorial for beginners, we will learn the basics of Python as a programming language, and understand how to get started with it. We will see how to download and install Python and use the popular IDEs to begin coding. We will also discuss jupyter functionality in detail.

What is Python used for?

Next time you are browsing through Google, indulging in your daily dose of Instagram, spending hours watching videos on Youtube, or listening to your favourite music on Spotify, remember that all of them use Python for their programming needs. Python has various uses across applications, platforms, and services. Let us talk about a few here.

Web Development

The large selection of pre-built Python libraries makes web development a much simpler task. Writing a Python code is less time-consuming due to its clean and simple syntax. This helps with quick prototyping accelerating the ROI of commercial products. The built-in testing frameworks help in shipping bug-free codes. A large selection of well-supported frameworks help in facilitating speedy implementation without compromising on the performance of the solution.

top Python web frameworks - python tutorial for beginners

Internet of Things

For the sake of simplicity, let us consider the Internet of Things to be the ‘physical objects connecting an embedded system to the internet’. It plays a vital role in projects that involve big data, machine learning, data analytics, wireless data networks, and cyber-physical systems. IoT projects also deal with real-time analytics.

A programming language should be a bold choice keeping in mind the aforementioned fields of application. This is where Python ticks off all the check-boxes. Additionally, Python is also scalable, expandable, portable, and embeddable. This makes Python system-independent and allows it to accommodate multiple single board computers, irrespective of the operating system or architecture.

Also, Python is excellent for managing and organising complex data. It is particularly useful for IoT systems that are data-heavy. Another reason for Python to be the ideal programming language for IoT applications is its close relation with scientific computing.

Python tools for IoT under python tutorial for beginners

Machine Learning

Machine Learning has offered a whole new approach to problem-solving. Python is at the forefront of Machine Learning and Data Science due to the following reasons:

  • Extensive open-source library support
  • Efficient and precise syntax
  • Easy integration with other programming languages
  • Python has a low entry-point
  • Scalable to different operating systems and architectures

How to download Python?

If you are a Windows user and if you have installed Python using Anaconda distribution package which is available at, you need to go to “Download Anaconda” and then download the latest version for Python 3.6.

Once you download this, it is a pretty simple and straightforward process to follow, and you will have Python installed for you. The next step is to power up an IDE to start coding in Python.

So once you install Python, you can have multiple IDEs or text editors on top of your Python installation.

For text editors, you can use something like Sublime or Notepad++. If you are comfortable using an Integrated Development Environment, then you can use Jupyter. Also, there are other options like Wingware, Komodo, Pycharm, and Spyder.

There are multiple packages available in Python. Some of the instrumental libraries are numpy, pandas, seaborn for visualisation and scipy for calculations and statistics. Others are xlrb, openpyxl, matplotlib, and io.


Why Python?

Python has become the most preferred programming language for enabling data science and machine learning applications. Of course, Python has its advantages; it is swift as compared to other programming languages, even R for that matter.

We can easily say that Python is a swift compiler. Since it is a Java-based programming language, you will be able to extend its applications beyond analytical research, analytical modelling, and statistical modelling. You will be able to create web applications using Python and integrate these web applications directly to your analytical models in the background.

Python is also very easy to integrate with other platforms and other programming languages. It has a common object-oriented programming architecture wherein existing IT developers, IT analysts, and IT programmers find it very easy to transition to the analytics domain.

Because the structure of coding in Python is object-oriented programing architecture, it has excellent documentation support.

7 Reasons Why You Should Use Python

  1. Readable and Maintainable Code
  2. Multiple Programming Paradigms
  3. Compatible with Major Platforms and Systems
  4. Robust Standard Library
  5. Open Source Frameworks and Tools
  6. Simplified Software Development
  7. Test-Driven Development

7 reasons why you should use Python - python tutorial for beginners

R vs Python?

R was developed for statistical analysis applications; on the other hand; Python was developed as a general-purpose programming language. Both of these are essential for those who work with large data-sets, solve machine learning problems, and create complex data visualisations.

Let us have a look at the differences between R and Python

python tutorial for beginners - R Vs. Python

Read more about the difference between R and Python, and which is a better alternative.

How fast can you learn python?

The ease of learning is the main attribute behind Python’s popularity. It is a simple and type free programming language and hence easy to learn. The time taken to learn the language depends on the level you want to achieve with Python. Also, the learning curve could be shorter or longer depending on individual ability.

One would require 6-8 weeks to learn the basics of Python. This will include learning the syntax, key-words, functions and classes, data types, basic coding, and exception handling.

Advanced Python skills are not necessary for all Python professionals. Depending on the nature of your work, you can learn skills such as database programming, socket programming, multithreading, synchronisation techniques etc.

The highly sophisticated Python skills include concepts of Data Analytics, hands-on experience of the required libraries, image processing etc. Each of the specialised skill would need around one week to master.

What are the top Python IDE?

Top 7 Python IDEs - python tutorial for beginners

Which is the best IDE for Python?

Jupyter is the best, and one of the most widely used IDE for Python. Let us have a look at how to set-up the Jupyter Notebook. Also, let us see what the functionalities of a Jupyter Notebook.

Powering up a Jupyter Notebook

Below are the guided steps to power up a Jupyter notebook:

  1. Open the Anaconda prompt. This is available to you if you have done the installation through the Anaconda installer. 
  2. Once you open the Anaconda Command Prompt, you will see a default path assigned to you. This is the username for the computer that you are using.
  3. Add the folder paths to this default path (e.g., cd Desktop → cd Python), where you want to open the notebook
  4. Once you set the path, add the Jupyter notebook using the command jupyter notebooksetting up a jupyter notebook - python tutorial for beginners
  5. Hit enter. This will open the notebook in your local host, i.e., your system
  6. The path described in the Anaconda prompt will now come on your jupyter notebook home page
  7. Next step is to open a new Python Notebook. This is your environment to carry out all the coding. You can rename the new notebook (untitled) to what you want and hit ‘rename’.

creating new python notebook - python tutorial for beginners

Keep the anaconda prompt active, the one which you used to power up your Jupyter notebook, while you are working with your Jupyter in your local. If the anaconda prompt is closed, the python is no longer running on your system, and the kernel gets disconnected.

Functionalities in a Python Notebook (Jupyter)

There are multiple options on the toolbar, i.e., File, Edit, View, Insert, Cell, Kernel, Widgets and Help. Let us have a look at some of the features and functionalities one by one.

File Options: 

File options in Jupyter notebook - python tutorial for beginners

Save and Checkpoint – Setting a Checkpoint is a fascinating concept. The file is Autosaved at regular intervals, and by setting a check-point, you can skip back a few auto-saves to the set checkpoint. This helps in case you made a mistake in the past couple of minutes or hours. You can always revert to a more stable checkpoint and proceed with your code from there, rather than starting from scratch. 

Download as – There are different ways in which you can download a Jupyter Notebook. First is the Classic Notebook, which is the ipynb extension. Before being called a jupyter notebook, it was an Ipython notebook. That is why this extension.

Then you have your .py extension. Save the file with .py extension, and you can import the same to a different IDE for easier use. 

downloading a Jupyter notebook - python tutorial for beginners

Close and Halt – This command closes whatever kernel is running at this particular point in time and halts all the processes.


Edit Options:

It includes Cut Cells, Copy Cells, Paste, Delete, Splitting a Cell, Moving up, down, so on and so forth. 

So, What is a cell?

Cells are nothing but the code that you type in the dialogue box present on the window. This is a cell, where you type in your code — each cell when run will give you an output. 

To run this particular piece of code, you can either click the specific option which says, Run cell or the shortcut key for the same is Shift + Enter

run cells in Jupyter notebook - python tutorial for beginners

If you want to explore the other available shortcut options, you can get under Help in Keyboard Shortcuts. 

keyboard shortcuts in Jupyter - python tutorial for beginners

You can cut these cells, paste them later on. You can merge, split, so on and so forth. These are simple items.


View Options:

You can Toggle your Headers, Toolbars, and Line numbers as well. 


Insert Options:

These are basic insert operations. You can insert a cell above or below as per the requirement of your code. 


Cell Options:

If you hit Run All, it runs all the cells that are present in this entire workbook. When you click ‘Run All Above’, it runs all the cells above the selected cell. Similarly, if you click ‘Run All Below’, it runs all the cells that are below the selected cell. 

The different types of cells, i.e., Code, Markdown and Raw Convert Files.

cell types in Jupyter - Python tutorial for beginnersOne exciting feature that we will be using much in our code files is something called Markdown file. A markdown is nothing but converting whatever you have typed in a cell into a text message.

The cells that you have converted as a Markdown will not be run or considered as a line of code. When you run this cell, it is taken as a text field, and the output is text too. No computation is carried out on this cell.

what is markdown - python tutorial for beginners


Help Options:

Here you can see the usual libraries and packages that are available.

help options in Jupyter - python tutorial for beginners

You can click on these options, and it will open a guidebook or the reference book, where you can have a look at the various methods that are available within the selected package.

There are various other options you can experiment with when you are working with Jupyter.

Learn python from expert faculty under Great Learning’s PG program in Artificial Intelligence and Machine Learning. You do not need any prior technical background to pursue this course and understand python functioning.


What is Artificial Intelligence?

Reading Time: 20 minutes

A Complete Beginner’s Guide to Artificial Intelligence

  1. What is Artificial Intelligence?
  2. How do we measure if the AI is acting like a human?
  3. How Artificial Intelligence works?
  4. What are the three types of Artificial Intelligence?
  5. What is the purpose of Artificial Intelligence?
  6. Where is AI used?
  7. What are the disadvantages of Artificial Intelligence?
  8. Applications of Artificial Intelligence in business?
  9. Career Trends in Artificial Intelligence
  10. What is Machine Learning?
  11. What are the different kinds of Machine Learning?
  12. What is Deep Learning?
  13. What is NLP?
  14. What is Python?
  15. What is Computer Vision?
  16. What are neural networks?

What is Artificial Intelligence?

The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own. An AI researcher would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, Artificial Intelligence is:

– An intelligent entity created by humans.

– Capable of performing tasks intelligently without being explicitly instructed.

– Capable of thinking and acting rationally and humanely.

How do we measure if the Artificial Intelligence is acting like a human?

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

– Turing Test

– The Cognitive Modelling Approach

– The Law of Thought Approach

– The Rational Agent Approach

What is Artificial Intelligence

Let’s take a detailed look at how these approaches perform:

What is the Turing Test?

The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent. The human agent ideally should not able to discern that they are talking to an Artificial Intelligence. To achieve these ends, the AI needs to possess these qualities:

Natural Language Processing to communicate successfully.

– Knowledge Representation to act as its memory.

– Automated Reasoning to use the stored information to answer questions and draw new conclusions.

– Machine Learning to detect patterns and adapt to new circumstances.

Cognitive Modelling Approach

As the name suggests, this approach tries to build an Artificial Intelligence model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:

– Introspection: observing our thoughts, and building a model based on that

– Psychological Experiments: conducting experiments on humans and  observing their behaviour

– Brain Imaging – Using MRI to observe how the brain functions in different scenarios and replicating that through code.

The Laws of Thought Approach

The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.

The Rational Agent Approach 

A rational agent acts to achieve the best possible outcome in its present circumstances.

According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.

Now that we understand how Artificial Intelligence can be designed to act like a human, let’s take a look at how these systems are built.

How Artificial Intelligence works?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of.  Artificial Intelligence can be built over a diverse set of components and will function as an amalgamation of:

– Philosophy

– Mathematics

– Economics

– Neuroscience

– Psychology

– Computer Engineering

– Control Theory and Cybernetics

– Linguistics

Let’s take a detailed look at each of these components.

What is Artificial Intelligence


The purpose of philosophy for humans is to help us understand our actions, their consequences, and how we can make better decisions. Modern intelligent systems can be built by following the different approaches of philosophy that will enable these systems to make the right decisions, mirroring the way that an ideal human being would think and behave. Philosophy would help these machines think and understand about the nature of knowledge itself. It would also help them make the connection between knowledge and action through goal-based analysis to achieve desirable outcomes.

Also Read: Artificial Intelligence and The Human Mind: When will they meet?


Mathematics is the language of the universe and system built to solve universal problems would need to be proficient in it. For machines to understand logic, computation, and probability are necessary.

The earliest algorithms were just mathematical pathways to make calculations easy, soon to be followed by theorems, hypotheses and more, which all followed a pre-defined logic to arrive at a computational output. The third mathematical application, probability, makes for accurate predictions of future outcomes on which Artificial Intelligence algorithms would base their decision-making.


Economics is the study of how people make choices according to their preferred outcomes. It’s not just about money, although money the medium of people’s preferences being manifested into the real world. There are many important concepts in economics, such as Design Theory, operations research and Markov decision processes. They all have contributed to our understanding of ‘rational agents’ and laws of thought, by using mathematics to show how these decisions are being made at large scales along with their collective outcomes are. These types of decision-theoretic techniques help build these intelligent systems.


Since neuroscience studies how the brain functions and Artificial Intelligence is trying to replicate the same, there’s an obvious overlap here. The biggest difference between human brains and machines is that computers are millions of times faster than the human brain, but the human brain still has the advantage in terms of storage capacity and interconnections. This advantage is slowly being closed with advances in computer hardware and more sophisticated software, but there’s still a big challenge to overcome as are still not aware of how to use computer resources to achieve the brain’s level of intelligence.


Psychology can be viewed as the middle point between neuroscience and philosophy. It tries to understand how our specially-configured and developed brain reacts to stimuli and responds to its environment, both of which are important to building an intelligent system. Cognitive psychology views the brain as an information processing device, operating based on beliefs and goals and beliefs, similar to how we would build an intelligence machine of our own.

Many cognitive theories have already been codified to build algorithms that power the chatbots of today.

Computer Engineering

The most obvious application here, but we’ve put this the end to help you understand what all this computer engineering is going to be based on. Computer engineering will translate all our theories and concepts into a machine-readable language so that it can make its computations to produce an output that we can understand. Each advance in computer engineering has opened up more possibilities to build even more powerful Artificial Intelligence systems, that are based on advanced operating systems, programming languages, information management systems, tools, and state-of-the-art hardware.

Control Theory and Cybernetics

To be truly intelligent, a system needs to be able to control and modify its actions to produce the desired output. The desired output in question is defined as an objective function, towards which the system will try to move towards, by continually modifying its actions based on the changes in its environment using mathematical computations and logic to measure and optimise its behaviours.


All thought is based on some language and is the most understandable representation of thoughts. Linguistics has led to the formation of natural language processing, that help machines understand our syntactic language, and also to produce output in a manner that is understandable to almost anyone. Understanding a language is more than just learning how sentences are structured, it also requires a knowledge of the subject matter and context, which has given rise to the knowledge representation branch of linguistics.

Read Also: Top 10 Artificial Intelligence Technologies in 2019

What are the 3 types of Artificial Intelligence?

Not all types of AI uses all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and that’s how they vary. The three broad types of AI are:

– Artificial Narrow Intelligence (ANI)

– Artificial General Intelligence (AGI)

– Artificial Super Intelligence (ASI)

What is Artificial Intelligence

Let’s take a detailed look.

What is Artificial Narrow Intelligence (ANI)?

This is the most common form of AI that you’d find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They’re able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

What is Artificial General Intelligence (AGI)?

AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

What is Artificial Super Intelligence (ASI)?

We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.

Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that’s how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.

What is the Purpose of Artificial Intelligence?

The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.

Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.

That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen.

Where is Artificial Intelligence used?

AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end user’s life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:

– Searching within data, and optimising the search to give the most relevant results

– Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters

– Pattern-detection to identify significant patterns in large data set for unique insights

– Applied probabilistic models for predicting future outcomes

What are the disadvantages of Artificial Intelligence?

As is the case with any new and emerging technology, AI has its fair share of drawbacks too such as:

– Cost overruns

– Dearth of talent

– Lack of practical products

– Lack of standards in software development

– Potential for misuse

Let’s take a closer look.

Cost overruns

What separates AI from normal software development is the scale at which they operate. As a result of this scale, the computing resources required would exponentially increase, pushing up the cost of the operation, which brings us to the next point.

Dearth of talent 

Since it’s still a fairly nascent field, there’s a lack of experienced professionals, and the best ones are quickly snapped up by corporations and research institutes. This increases the talent cost, which further drives up Artificial Intelligence implementation prices.

Lack of practical products

For all the hype that’s been surrounding AI, it doesn’t seem to have a lot to show for it. Granted that applications such as chatbots and recommendation engines do exist, but the applications don’t seem to extend beyond that. This makes it difficult to make a case for pouring in more money to improve AI capabilities.

Lack of standards in software development

The true value of Artificial Intelligence lays in collaboration when different AI systems come together to form a bigger, more valuable application. But a lack of standards in AI software development means that it’s difficult for different systems to ‘talk’ to each other. Artificial Intelligence software development itself is slow and expensive because of this, which further acts as an impediment to AI development.

Potential for Misuse

The power of Artificial Intelligence is massive, and it has the potential to achieve great things. Unfortunately, it also has the potential to be misused. Artificial Intelligence by itself is a neutral tool that can be used for anything, but if it falls into the wrong hands, it would have serious repercussions. In this nascent stage where the ramifications of AI developments are still not completely understood, the potential for misuse might be even higher.

Applications of Artificial Intelligence in business?

AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since Artificial Intelligence is an efficient data processing system at its core, there’s a lot of potential for optimisation everywhere.

What is Artificial Intelligence

Let’s take a look at the industries where AI is currently shining.


– Administration: AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it.

– Telemedicine: For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them.

– Assisted Diagnosis: Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error.

– Robot-assisted surgery: Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering.

– Vital Stats Monitoring:  A person’s state of health is an ongoing process, depending on the varying levels of their respective vitals stats. With wearable devices achieving mass-market popularity now, this data is not available on tap, just waiting to be analysed to deliver actionable insights. Since vital signs have the potential to predict health fluctuations even before the patient is aware, there are a lot of live-saving applications here.


– Better recommendations: This is usually the first example that people give when asked about business applications of AI, and that’s because it’s an area where AI has delivered great results already. Most large e-commerce players have incorporated Artificial Intelligence to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-lines.

– Chatbots: Another famous example, based on the proliferation of Artificial Intelligence chatbots across industries, and every other website we seem to visit. These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources.

– Filtering spam and fake reviews: Due to the high volume of reviews that sites like Amazon receive, it would be impossible for human eyes to scan through them to filter out malicious content. Through the power of NLP, Artificial Intelligence can scan these reviews for suspicious activities and filter them out, making for a better buyer experience.

– Optimising search: All of the e-commerce depends upon users searching for what they want, and being able to find it. Artificial Intelligence has been optimising search results based on thousands of parameters to ensure that users find the exact product that they are looking for.

– Supply-chain: AI is being used to predict demand for different products in different timeframes so that they can manage their stocks to meet the demand.

Human Resources 

– Building work culture: AI is being used to analyse employee data and place them in the right teams, assign projects based on their competencies, collect feedback about the workplace, and even try to predict if they’re on the verge of quitting their company.  

– Hiring: With NLP, AI can go through thousands of CV in a matter of seconds, and ascertain if there’s a good fit. This is beneficial because it would be devoid of any human errors or biases, and would considerably reduce the length of hiring cycles.

What is Artificial Intelligence

Career Trends in Artificial Intelligence

Careers in AI have been steadily increasing over the past few years and will continue growing at an accelerating rate. 57% of Indian companies are looking forward to hiring the right talent to match up the Market Sentiment. On an average, there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles. Mumbai stays tall in the competition followed by Bangalore and Chennai. As per a research, the demand for AI Jobs have increased but efficient workforce has not been keeping pace with it. As per WEF, 133 million jobs would be created in Artificial Intelligence by the year 2020.


Artificial Intelligence Career Trends 2019
What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of Artificial Intelligence – the ability to learn from experience, rather than just instructions.

Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output.  They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

What are the different kinds of Machine Learning?

The types of Machine learning are:

– Supervised Learning

– Unsupervised Learning

– Semi-supervised learning

– Reinforcement Learning

Also Read: Advantages of pursuing a career in Machine Learning

What is Supervised Learning?

Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.

This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.

What is Unsupervised Learning?

With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.

What is Semi-supervised Learning?

This is similar to the above two, with the only difference being that it uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.

Read Also: Top 9 Artificial Intelligence Startups in India

What is Reinforcement Learning?

Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.

What is Artificial Intelligence

What is Deep Learning?

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.

Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.

Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

Deep Learning vs. Machine Learning

What is Artificial Intelligence

How is Deep Learning Used- Applications

Deep Learning applications have started to surface but have a much greater scope for the future. Listed here are some of the deep learning applications that will rule the future.

– Adding image and video elements – Deep learning algorithms are being developed to add colour to the black and white images. Also, automatically adding sounds to movies and video clips.

– Automatic Machine Translations – Automatically translating text into other languages or translating images to text. Though automatic machine translations have been around for some time, deep learning is achieving top results.

– Object Classification and Detection – This technology helps in applications like face detection for attendance systems in schools, or spotting criminals through surveillance cameras. Object classification and detection are achieved by using very large convolutional neural networks and have use-cases in many industries.

– Automatic Text Generation – A large corpus of text is learnt by the machine learning algorithm and this text is used to write new text. The model is highly productive in generating meaningful text and can even map the tonality of the corpus in the output text.

– Self-Driving cars – A lot has been said and heard about self-driving cars and is probably the most popular application of deep learning. Here the model needs to learn from a large set of data to understand all key parts of driving, hence deep learning algorithms are used to improve performance as more and more input data is fed.

– Applications in Healthcare – Deep Learning shows promising results in detecting chronic illnesses such as breast cancer and skin cancer. It also has a great scope in mobile and monitoring apps, and prediction and personalised medicine.

Why is Deep Learning important?

Today we can teach machines how to read, write, see, and hear by pushing enough data into learning models and make these machines respond the way humans do, or even better. Access to unlimited computational power backed by the availability of a large amount of data generated through smartphones and the internet has made it possible to employ deep learning applications into real-life problems.

This is the time for deep learning explosion and tech leaders like Google are already applying it anywhere and everywhere possible.

The performance of a deep learning model improves with an increase in the amount of input data as compared to Machine Learning models, where performance tends to decline with an increase in the amount of input data.

What is Artificial Intelligence

What is NLP?

A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.

Read Also: Most Promising Roles for Artificial Intelligence in India

What are the different steps involved in NLP?

The steps involved in implementing NLP are:

– The computer program collects all the data required. This includes database files, spreadsheets, email communication chains, recorded phone conversations, notes, and all other relevant data.

– An algorithm is employed to remove all the stop words from this data and normalizes certain words which have the same meaning.

– The remaining text is divided into groups known as tokens.

– The NLP program analyzes results to spot deduce patterns, their frequency and other statistics to understand the usage of tokens and their applicability.

Where is NLP used?

Some of the common applications that are being driven by Natural Language Processing are:

– Language translation application

– Word processors to check grammatical accuracy of the text

– Call centres use Interactive Voice Response to respond to user requests; IVR is an application NLP

– Personal virtual assistants such as Siri and Cortana are a classic example of NLP

What is Python?

Python is a popular object-oriented programming language that was created by Guido Van Rossum and released in the year 1991. It is one of the most widely-used programming languages for web development, software development, system scripting, and many other applications.

Why is Python so popular?

What is Artificial Intelligence

There are many reasons behind the popularity of Python as the much-preferred programming language, i.e.,

– The easy to learn syntax helps with improved readability and hence the reduced cost of program maintenance

– It supports modules and packages to encourage code re-use

– It enables increased productivity as there is no compilation step making the edit-test-debug cycle incredibly faster

– Debugging in Python is much easier as compared to other programming languages

Read Also: Top Interview Questions for Python

Where is Python used?

Python is used in many real-world applications such as:

– Web and Internet Development

– Applications in Desktop GUI

– Science and Numeric Applications

– Software Development Applications

– Applications in Business

– Applications in Education

– Database Access

– Games and 3D Graphics

– Network Programming

How can I learn Python?

There is a lot of content online in the form of videos, blogs, and e-books to learn Python. You can extract as much information as you can through the online material as you can and want. But, if you want more practical learning in a guided format, you can sign up for Python courses provided by many ed-tech companies and learn Python along with hands-on learning through projects from an expert which would be your mentor. There are many offline classroom courses available too. Great Learning’s Artificial Intelligence and Machine Learning course have an elaborate module on Python which is delivered along with projects and lab sessions.

What is Computer Vision?

Computer Vision is a field of study where techniques are developed enabling computers to ‘see’ and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.

What is Computer Vision used for?

There are many applications of Computer Vision today, and the future holds an immense scope.

– Facial Recognition for surveillance and security systems

– Retail stores also use computer vision for tracking inventory and customers

– Autonomous Vehicles

– Computer Vision in medicine is used for diagnosing diseases

– Financial Institutions use computer vision to prevent fraud, allow mobile deposits, and display information visually

What is Deep Learning Computer Vision

Following are the uses of deep learning for computer vision:

What is Artificial Intelligence

Object Classification and Localisation: It involves identifying the objects of specific classes of images or videos along with their location highlighted usually with a square box around them.

Semantic Segmentation: It involves neural networks to classify and locate all the pixels in an image or video.

Colourisation: Converting greyscale images to full-colour images.

Reconstructing Images: Reconstructing corrupted and tampered images.

What are Neural Networks?

Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.

Read Also: A Peek into Global Artificial Intelligence Strategies

What are Neural Networks used for?

The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.

What are the different Neural Networks?

What is Artificial Intelligence

The different types of neural networks are:

Feedforward Neural Network: Artificial Neuron: Here data inputs travel in just one direction, going in from the input node and exiting on the output node.

Radial basis function Neural Network: For their functioning, the radial basis function neural network consider the distance between a point from the centre.

Kohonen Self Organizing Neural Network: The objective here is to input vectors of arbitrary dimension to discrete map comprised of neurons.

– Recurrent Neural Network(RNN): The Recurrent Neural Network saves the output of a layer and feeds it back to the input for helping in predicting the output of the layer.

– Convolutional Neural Network: It is similar to the feed-forward neural networks with the neurons having learn-able biases and weights. It is applied in signal and image processing.

– Modular Neural Networks: It is a collection of many different neural networks, each processing a sub-task. Each of them has a unique set of inputs as compared to other neural networks contributing towards the output.

What are the benefits of Neural Networks?

The three key benefits of neural networks are:

– The ability to learn and model non-linear and complex relationships

– ANNs can generalize models infer unseen relationships on unseen data as well

– ANN does not impose any restrictions on the input variables

Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Hypothesis Testing in R- with examples and case study

Reading Time: 10 minutes

Hypothesis Testing is an essential part of statistics for Data Analytics. It is a statistical method used to make decisions based on statistics drawn from experimental data. In simple terms, it can also be called an educated claim or statement about a property or population.

The goal of Hypothesis Testing in R is to analyze a sample in an attempt to distinguish between population characteristics, that are likely to occur and population characteristics that are unlikely to occur.

Key terms and concepts-

Null Hypothesis H0:

A null hypothesis is the status quo

A general statement or default position that there is no relationship between two measured phenomena or no association among groups.

Alternate Hypothesis H1:

The alternative hypothesis is contrary to the Null Hypothesis.

It is usually believed that the observations are the result of a real effect.

Null and Alternative Hypotheses Examples:

IndustryNull HypothesisAlternate Hypothesis
Process IndustryShop Floor Manager in Dairy Company feels that the Milk Packaging Process unit for 1 Litre Packs is working fine and does not need any calibration. SD = 10 ml Null Hypothesis : µ = 1Alternate Hypothesis: µ ≠ 1
Credit RiskCredit Team of a Bank has been taking lending decisions based on in-house developed Credit Scorecard. Their claim to fame in the organization is their scorecard has helped reduce NPAs by at least 0.25%

Null Hypothesis: Scorecard has not helped in reducing NPA level π (scorecard NPA) – π (No scorecard NPA) = 0.25%

Alternate Hypothesis : π (scorecard NPA) – π (No scorecard NPA) > 0.25%
Motor IndustryAn Electric Car manufacturer claims their newly launched e-car gives an average mileage of 125 MPGe (Miles per Gasoline Equivalent)

Null Hypothesis : µ = 125

Alternate Hypothesis: µ < 125


Type I and Type II Error

Null HypothesisTrueFALSE
RejectType I Error (α)No error
AcceptNo errorType II Error (β)


A Null hypothesis is rejected when it is True. This is Type 1 Error

Eg: A manufacturer’s Quality Control department rejects a lot when it has met the market acceptable quality level. This is the Producer’s Risk.


Type I and Type II Error

Null HypothesisTrueFALSE
RejectType I Error (α)No error
AcceptNo errorType II Error (β)


The Null Hypothesis is accepted when it is False. This is Type II Error

E.g. A Consumer accepts a lot when it is faulty. This is Consumer’s Risk


Type I and Type II Error

Hypthesis testing in R 1 - Type 1 and Type 2 error


Hypotheis testing in R2 - Type A and Type B error


In statistical significance testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was observed, assuming that the null hypothesis is correct.

Significance value:

The probability of rejecting the null hypothesis is when it is called the significance level α.

Note: If the P-value is equal to or smaller than the significance level(α), it suggests that the observed data is inconsistent with the assumption that the null hypothesis is correct and thus this hypothesis must be rejected (but this does not automatically mean the alternative hypothesis can be accepted as true).

α is the probability of Type I error and β is the probability of Type ll error.

The experimenters have the freedom to set the α level for a particular hypothesis test.

That level is called the level of significance for the test. Changing α can (and often does) affect the results of the test- whether you reject or fail to reject H0.

As α increases, β decreases and vice versa.

The only way to decrease both α and β is to increase the sample size. To make both quantities equal to zero, the sample size would have to be infinite, and you would have to sample the entire population.

Possible Errors while HT

The confidence coefficient (1-α) is the probability of not rejecting H0 when it is True.

The Confidence level of a hypothesis test is (1-α) * 100%.

The power of a statistical test (1-β) is the probability of rejecting H0 when it is false.

How to correct errors in HT 

Steps to follow:

Determine a P-value when testing a Null Hypothesis

If the alternative hypothesis is less than the alternative, you reject H0 only if the test statistic falls in the left tail of the distribution (below 2). Similarly, if H1 is higher than the alternative, you reject the H0 only if the test statistic falls in the right tail (above 2)

Hypothesis Testing in R

Upper tailed, Lower Tailed, Two-Tailed Tests

H1: µ >  µ0, where µ0 is the comparator or null value and an increase is hypothesized –this type of test is called an upper-tailed test.

H1: µ <  µ0, where a decrease is hypothesized and this type of test is called a lower-tailed test.

H1: µ ≠ µ0, where a difference is hypothesized and this type of test is called a two-tailed test.


Types of T-test in Hypothesis Test:

One sample t-test – One sample t-test is used to compare the mean of a population to a specified theoretical mean µ

The unpaired two-sample t-test (Independent t-test) – Independent (or unpaired two-sample) t-test is used to compare the means of two unrelated groups of samples.

Paired t-test – Paired Student’s t-test is used to compare the means of two related samples. That is when you have two values (a pair of values) for the same samples.

Let’s Look at some Case studies:

t-Test Application One Sample

Experience Marketing Services reported that the typical American spends a mean of 144 minutes (2.4 hours) per day accessing the Internet via a mobile device. (Source: The 2014 Digital Marketer, available at To test the validity of this statement, you select a sample of 30 friends and family. The result for the time spent per day accessing the Internet via a mobile device (in minutes) are stored in Internet_Mobile_Time.csv file.

Is there evidence that the populations mean time spent per day accessing the Internet via a mobile device is different from 144 minutes? Use the p-value approach and a level of significance of 0.05

What assumption about the population distribution is needed to conduct the test in A?

Solution In R


> Mydata=read.csv("InternetMobileTime.csv", header = TRUE)

Hypothesis Testing in R

> attach(mydata)

> xbar=mean(Minutes)

> s=sd(Minutes)

> n=length(Minutes)

> Mu=144 #null hypothesis

> tstat=(xbar-Mu)/(s/(n^0.5))

> tstat

[1] 1.224674

> Pvalue=2*pt(tstat, df=n-1, lower=FALSE)

> Pvalue

[1] 0.2305533

> if(Pvalue<0.05)NullHypothesis else "Accepted"

[1] “Accepted”

  1. Independent t-test two sample

A hotel manager looks to enhance the initial impressions that hotel guests have when they check-in. Contributing to initial impressions is the time it takes to deliver a guest’s luggage to the room after check-in. A random sample of 20 deliveries on a particular day was selected each from Wing A and Wing B of the hotel. The data collated is given in Luggage.csv file. Analyze the data and determine whether there is a difference in the mean delivery times in the two wings of the hotel. (use alpha = 0.05).

Solution In R

Hypothesis Testing in R

> t.test(WingA,WingB, var.equal = TRUE, alternative = "greater")

    Two Sample t-test

data:  WingA and WingB

t = 5.1615,

df = 38,

p-value = 4.004e-06

alternative hypothesis: true difference in means is greater than 0

95 percent confidence interval:

1.531895   Inf

sample estimates:

mean of x mean of y

 10.3975 8.1225

> t.test(WingA,WingB)

   Welch Two Sample t-test

data:  WingA and WingB

t = 5.1615, df = 37.957, p-value = 8.031e-06

alternative hypothesis: true difference in means is not equal to 0

95 per cent confidence interval:

1.38269 3.16731

sample estimates:

mean of x mean of y

 10.3975 8.1225

boxplot(WingA,WingB, col = c("Red","Pink"), horizontal = TRUE)

Hypothesis Testing in R 

Case Study- Titan Insurance Company

The Titan Insurance Company has just installed a new incentive payment scheme for its lift policy salesforce. It wants to have an early view of the success or failure of the new scheme. Indications are that the sales force is selling more policies, but sales always vary in an unpredictable pattern from month to month and it is not clear that the scheme has made a significant difference.

Life Insurance companies typically measure the monthly output of a salesperson as the total sum assured for the policies sold by that person during the month. For example, suppose salesperson X has, in the month, sold seven policies for which the sums assured are £1000, £2500, £3000, £5000, £10000, £35000. X’s output for the month is the total of these sums assured, £61,500. Titan’s new scheme is that the sales force receives low regular salaries but are paid large bonuses related to their output (i.e. to the total sum assured of policies sold by them). The scheme is expensive for the company, but they are looking for sales increases which more than compensate. The agreement with the sales force is that if the scheme does not at least break even for the company, it will be abandoned after six months.

The scheme has now been in operation for four months. It has settled down after fluctuations in the first two months due to the changeover.

To test the effectiveness of the scheme, Titan has taken a random sample of 30 salespeople measured their output in the penultimate month before changeover and then measured it in the fourth month after the changeover (they have deliberately chosen months not too close to the changeover). The outputs of the salespeople are shown in Table 1



Data preparation

Since the given data are in 000, it will be better to convert them in thousands.

Hypothesis Testing in R

Problem 1

Describe the five per cent significance test you would apply to these data to determine whether the new scheme has significantly raised outputs? What conclusion does the test lead to?


It is asked that whether the new scheme has significantly raised the output, it is an example of the one-tailed t-test.

Note: Two-tailed test could have been used if it was asked “new scheme has significantly changed the output”

Mean of amount assured before the introduction of scheme = 68450

Mean of amount assured after the introduction of scheme = 72000

Difference in mean = 72000 – 68450 = 3550


μ1 = Average sums assured by salesperson BEFORE changeover. μ2 = Average sums assured by salesperson AFTER changeover.

H0: μ1 = μ2  ; μ2 – μ1 = 0

HA: μ1 < μ2   ; μ2 – μ1 > 0 ; true difference of means is greater than zero.

Since population standard deviation is unknown, paired sample t-test will be used.

Hypothesis Testing in R


Since p-value (=0.06529) is higher than 0.05, we accept (fail to reject) NULL hypothesis. The new scheme has NOT significantly raised outputs.

Problem 2

Suppose it has been calculated that for Titan to break even, the average output must increase by £5000. If this figure is an alternative hypothesis, what is:

(a)  The probability of a type 1 error?

(b)  What is the p-value of the hypothesis test if we test for a difference of $5000?

(c)   Power of the test:


2.a.  The probability of a type 1 error?

Solution: Probability of Type I error = significant level = 0.05 or 5%

2.b.  What is the p-value of the hypothesis test if we test for a difference of $5000?


Let  μ2 = Average sums assured by salesperson AFTER changeover.

μ1 = Average sums assured by salesperson BEFORE changeover.

μd = μ2 – μ1   H0: μd ≤ 5000 HA: μd > 5000

This is a right tail test.

R code:

Hypothesis Testing in R


P-value = 0.6499

2.c. Power of the test.


Let  μ2 = Average sums assured by salesperson AFTER changeover. μ1 = Average sums assured by salesperson BEFORE changeover. μd = μ2 – μ1   H0: μd = 4000

HA: μd > 0

H0 will be rejected if test statistics > t_critical.

With α = 0.05 and df = 29, critical value for t statistic (or t_critical ) will be   1.699127.

Hence, H0 will be rejected for test statistics ≥  1.699127.

Hence, H0 will be rejected if for  𝑥̅ ≥ 4368.176


Hypothesis Testing in R

R Code:

Hypothesis Testing in R

      Probability (type II error) is P(Do not reject H0 | H0 is false)

      Our NULL hypothesis is TRUE at μd = 0 so that  H0: μd = 0 ; HA: μd > 0

      Probability of type II error at μd = 5000

= P (Do not reject H0 | H0 is false)

= P (Do not reject H0 | μd = 5000)  = P (𝑥̅ < 4368.176 | μd = 5000)

= P (t <  | μd = 5000)

= P (t < -0.245766)

= 0.4037973

R Code:

Hypothesis Testing in R

Hypothesis Testing in R


Now,  β=0.5934752,

Power of test = 1- β = 1- 0.5934752

= 0.4065248



Hypothesis testing in R should always explain what is expected to happen. It contains both an independent and dependent variable. It should be testable and measurable,  but it may or may not be correct. You have to ascertain the truth of the hypothesis by using Hypothesis Testing.

Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps.

  • Formulate the null hypothesis (commonly, that the observations are the result of pure chance) and the alternate hypothesis (commonly, that the observations show a real effect combined with a component of chance variation).
  • Identify a test statistic that can be used to assess the truth of the null hypothesis
  • Compute the p-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis was correct. The smaller the p-value, the stronger the evidence against the null hypothesis.
  • Compare the p-value to an acceptable significance value a (sometimes called an alpha value). If p <= a, then the observed effect is statistically significant, i.e., the null hypothesis is ruled out, and the alternative hypothesis is valid.


21 Open Source Python Libraries You Should Know About

Reading Time: 7 minutes

The probability that you must have heard of ‘Python’ is outright. Guido Van Rossum’s brainchild – Python, which dates back to the ’80s has become an avid game changer. It is one of the most popular coding languages today and is widely used for a gamut of applications. In this article, we have listed 21 Open Source Python Libraries you should know about.

What is a Library

A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python. 

Let’s Get Started!

1. Scikit- learn: It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.

Scikit-learn requires:

  • Python (>= 2.7 or >= 3.3),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).

python libraries

Spotify uses Scikit-learn for its music recommendations and Evernote for building their classifiers. If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip.

2. NuPIC: The Numenta Platform for Intelligent Computing (NuPIC) is a platform which aims to implement an HTM learning algorithm and make them public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex. Click here to check their code on GitHub.

3. Ramp: It is a Python library which is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.

4. NumPy: When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

21 open source python libraries you should know

The various NumPy installation packages can be found here.

5. Pipenv: The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.

6. TensorFlow: The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within Google AI organisation, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

7. Bob: Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

8. PyTorch: Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features – Tensor computation (like numpy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

9. PyBrain: PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.

21 Open Source Python Libraries you should know

10. MILK: This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, decision trees. A range of combination of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.

11. Keras: It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install TensorFlow backend engine.

12. Dash: From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. This productive Python framework is ideal for data visualization apps particularly suited for every Python user. The ease which we experience is a result of extensive and exhaustive effort.

13. Pandas: It is an open-source, BSD licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modelling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. The best way to install Pandas is by Conda installation 

python libraries

14. Scipy: This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high-performance computing and quality assurance. The various installation packages can be found here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

15. Matplotlib: All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code.

python libraries

The various installation packages can be found here.

16. Theano: This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. For a humongous volume of data, handcrafted C codes become slower. Theano enables swift implementations of code. Theano can recognise unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. Follow the link to read more about Theano. The closest Python package to Theano is Sympy. So let us talk about it.

17. SymPy: For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is written in Python only and can be embedded in other applications and extended with custom functions. You can find the source code on GitHub. 

18. Caffe2: The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ API’s in Caffe2, we can create our prototype now and optimize later. You can get started with Caffe2 now with this step-by-step installation guide.

19. Seaborn: When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures. Visit the installation page to see how this package can be installed

20. Hebel: This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future. Follow the link to explore Hebel.

21. Chainer: A competitor to Hebel, this Python package aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :

a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.

b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.

c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells.

The installation, projects and other details can be found here.

So here is a list of the common Python Libraries which are worth taking a peek at and if possible familiarizing yourself with. If you feel there is some library which deserves to be in the list do not forget to mention it in the comments.


How Boredom Led To The Creation Of Python

Reading Time: 4 minutes

If necessity is the mother of invention, boredom is the father. From toothpaste with a squeezer to cupcakes, from mirror wipers to pizza scissors, it was sheer boredom that led to these wacky inventions.

Boredom always precedes a period of great creativity. Believe it or not, it was the boredom of a single man in the late 80s, that led to one of the ‘most searched words on Google’ surpassing ‘selfie’ in 2018 – Python.

In every great dynasty, there comes a successor, who in time, becomes the predecessor, and then the cycle repeats. With emerging technologies inclining towards Artificial Intelligence, Machine Learning and Deep Learning, Python has become a clear dominator.

Programming is a hard shell to crack when it is not your forte. At times, daunting and repulsive as well. It has been observed that Python has gained popularity not only among technical gurus but among amateurs as well. About 95% of all the data online was generated in the last 2 years. And with such massive amounts of data, the chances of securing a job have increased substantially. Python is a comparatively easy language to learn. Several libraries in Python are specifically written for Data Science, and this allows you to keep your coding to the minimum and yet get meaningful results.

The 2 main advantages of the language are its simplicity and flexibility. It’s simple and straightforward syntax and use of indented space makes it easy to learn, read and share. The people who practice the language, AKA Pythonistas, have uploaded 145,000 custom-built software packages to an online repository. Also called ‘library’. These cover everything from game development to astronomy and can be installed and inserted into a Python program in a matter of seconds.

To put things in perspective, in the past 12 months, the Google search for Python has surpassed the Google search for Kim Kardashian! Python was the top-ranked language on the Tiobe Index in 2007 and 2010, as is among the top contenders this year as well. The increase in job seekers’ interest in Python has witnessed a steep rise, surpassing R and SaS as of January 2018.

So What’s Next?

Python dates back to the late 80s and it was first implemented in 1989 by Guido van Rossum, the creator of Python, as a successor to ABC language, which is an imperative general purpose programming language. What makes Python desirable is its simplicity, flexibility, compatibility, versatility and the fact that it is free and the numerous open source libraries are the icing on the cake.

The language can be used for almost anything: from the basic ‘hello world!’ algorithm to complex Machine Learning algorithms for Face Recognition, Drone Imagery, Internet of Things, Gaming, Robotics, Natural Language Processing, healthcare and many more. Moreover, the code is concise and easily understandable even for those who have never written it before, at least in the nascent stages.

With such a rapidly growing user base, Python might seem destined to become the lingua franca of coding, rendering all other competitors obsolete. Having said that, researchers still believe that it is unlikely that Python will replace C or C++, which provide the user complete control over what is going on inside the processor, nor will it replace Java or Javascript which power most web pages today.

Although we can see Java at the top of the list, a more lucrative observation is the steady decline and the steep incline in the use of Java and Python respectively. Even though Python has become ubiquitous, its competitors haven’t left the battleground. From lower level languages like C and C++ to Java and Javascript, Python still has a tough competition to be wary of.

But in a surprising turn of events:

Guido Van Rossum to sys.exit()s due to overload.

Yes, Van Rossum is no longer associated with Python.

“Now that PEP 572 is done, I don’t ever want to have to fight so hard for a PEP and find that so many people despise my decisions.” – Guido Van Rossum.

A PEP is a Python Enhancement Proposal, which is similar to the Whatsapp update we receive as it upgrades Python with new features. He was also quoted saying, “I am not going to appoint a successor.”

So does this render the Great Python Dynasty, one without a successor? Only time will tell.

If you are into the early stage of your graduation or are planning to take up a course, you should definitely consider learning Python for Machine Learning as your first option.