This machine learning course is designed by Vidyasagar Bhargava.
Prerequiste
Week 0 - Python Fundamentals
Week 1 - Python for data science
Week 2 - Maths for Machine Learning
Week 3 - Statistics for Machine Learning
Linear Models and ML concepts
Week 4 - Data Treatment (Missing Values, Outliers, Categorical Features)
Week 5 - Linear Regression
Week 6 - Logistic Regression
Week 7 - Advanced Regression Techniques
Week 8 - Feature Engineering + Feature Scaling + Feature Selection
Week 9 - Hyperparameter tuning and Cross Validation techniques
Week 10 - Regularization Techniques [Lasso, Ridge & Elastic Net]
Week 11 - Kaggle Competitions-1 [Titanic & House Pricing Challenge]
Unsupervised Algorithms
Week 12 - Clustering Algorithms
Week 13 - SVD and PCA
Tree based Advanced Algorithms
Week 14 - Decision Tree
Week 15 - Random Forest
Week 16 - Model Explainability
Week 17 - Boosting Algorithms-I
Week 18 - Boosting Algorithms-II
Week 19 - Kaggle Competition-2 [Amex & Instacart Challlenge]
Deep Learning & NLP
Week 20 - Deep Learning & CNN Basics
Week 21 - Optimization Techniques [Adam, RMSPROP SGD etc]
Week 22 - Working with Text Data
Week 23 - Sequence Models
Week 24 - Transformers & Bert Models
Week 25 - Kaggle Competition-3 [Fashion MNIST & Quora]
Machine Learning System Design
TBA
Some other topics
Imbalanced dataset
Loss functions and types
Bias Variance Trade off
Data Augmentation
Batch Normalization
Initialization of Weights
Distribution Shift
Data leakage
Distributed Model Training