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Course Overview
Course Overview
Software Installation
Python Basics
Python Basics 1
Python Basics 2
Numpy
Numpy
Pandas
Pandas
Matplotlib and Seaborn
1. Matplotlib
2. Seaborn
Probability and Statistics
1. probability final
2. statistics1
Introduction to ML Algos
Machine learning Overview
Linear Regression
1. Linear Regression Theory
2. implementation of gradient descent
3. feature scaling
4. Linear regression Practical
5. Multivariate linear regression
K Nearest Neighbours
KNN Theory
KNN practical
Decision Trees
Decision Tree Theory
Decision Tree Use case
Logistic Regression
1. Logistic Regression
2. Logistic Regression
3. Logistic Regression
4. Logistic Regression
5. Logistic Regression
6. Logistic Regression Practical
Naive Bayes
1. Naive Bayes
2. Naive Bayes real life use case
3. Gaussian Naive Bayes
Scikit Learn
1. Scikit learn tutorial
2. MNIST dataset
3. Logistic Regression scikit learn
Feature Engineering
1. Feature Engineering Over view
2. Outliers
3. Missing values
4. Missing value 2
5. Categorical Encodings Theory
6. Encoding Practical
7. Feature slection theory
8. Feature Selection Practical
9. Data visualization application final
10. Final project on titanic data - 1
11. Titanic - data pre processing and model creation
12. Telecom Customer Churn - Data visualization and EDA
13. Telecom Churn Analysis 2
Deep Learning
1. Neural Networks 1
2. Neural Network 2
3. Activation Functions - Tan H
4. Activation Function ReLu
5. Activation Function - Softmax -
6. Deep Learning Practical
7. CNN intuition