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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. K - Nearest Neighbours
  5. Linear Regression
  6. Logistic Regression
  7. Gradient Descent
  8. Decision Trees
  9. Support Vector Machines
  10. Model Productionisation
  11. Models with Feature Engineering
  12. Hyperparameter Tuning
  13. Ensembles
  14. Clustering
  15. Principal component Analysis

Module 5 - Case Studies

Module 6 - Deep Learning

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