Getting started with Machine Learning and Deep Learning

Module 1 - Python Programming

1. Intro to Python
2. Data Structures in Python (List, Tuple, Set, Dictionary)
3. Control Statements (Decision and Loops)
4. Functions and Modules
5. Object Oriented Programming
6. Exception Handling
7. File Handling
8. Web API
9. Databases
10. List Comprehension, Lambda, Filter, Map, Reduce
11. Problem Solving for Interviews

Module 2 - Python for Data Analysis

1. Data Analytics Framework
2. Numpy
3. Pandas for Beginners
4. Advance Pandas Operations
5. Case Study - Pandas Manipulation
6. Missing Value Treatment
7. Visuallization Basics - Matplotlib and Seaborn
8. Case Study - Covid_19_TimeSeries
9. Plotly and Express
10. Outliers - Coming Soon

Module 3 - Statistics for Data Analysis

1. Normal Distribution
2. Central Limit Theorem
3. Hypothesis Testing
4. Chi Square Testing
5. Performing Statistical Test

Module 4 - Machine Learning

1. Data Preparation and Modelling with SKLearn
2. Working with Text Data
3. Working with Image Data
4. Supervised ML Algorithms
   - K - Nearest Neighbours
   - Linear Regression
   - Logistic Regression
   - Gradient Descent
   - Decision Trees
   - Support Vector Machines
   - Models with Feature Engineering
   - Hyperparameter Tuning
   - Ensembles
5. Model Productionisation
6. Unsupervised ML Algorithms
   - Clustering
   - Principal Component Analysis

Module 5 - Case Studies

• Car Price Prediction (Regression)
• Airline Sentiment Analysis (NLP - Classification)
• Adult Income Prediction (Classification)
• Web App Development + Serialization and Deserialization
• Streamlit Heroku Deployment
• Web Scrapping

Module 6 - Deep Learning

• Introduction to Deep Learning
• Training a Deep Neural Network + TensorFlow.Keras
• Convolutional Neural Network + TensorFlow.Keras
• Recurrent Neural Network (Coming Soon)