Preview Machine Learning and Data Science with Python

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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

Preview - Machine Learning and Data Science with Python