About this course
Machine learning uses two types of techniques: one is supervised learning which trains a model on known input and output data so that it can predict future outputs. The second called unsupervised learning finds hidden patterns or intrinsic structures in input data. In this course, you will learn some of the most popular supervised learning algorithms such as KNN and Naive Bayes.
Naive Bayes Classifier
Machine Learning Foundation
- Concepts of machine learning and its importance
- Feature or Mathematical space
- Introduction to Supervised machine learning
- Linear regression and it’s Pearson’s coefficient
- Linear regression mathematically and coefficient of Determinant
- Advantages and Disadvantages of Linear Regression
- Brief scenario of Data set and Descriptive analysis-3
- Analyse the Distribution of dependent column
- Missing Values imputation
- Bivariate analysis using plots through Seaborn function
- Building model using all information
- Cleaning the data, plotting graphs and some mathematical expressions
- Analysis of model and concept of Squared errors
- Concept of fluke correlation
- Logit function in Logistic regression
- Probability examples and model predictions
- Hands-on exercise on logistic regression
- Introduction to Naive Bayes Classifier
- Naive Baye’s Classifier and its example with 2 dimension
- Bayes theorem and formula
Get Machine Learning Foundations course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
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