30 SESSIONS*

1. Introduction to ML
2. Supervised Learning
   • • Linear Regression
     • Logistic Regression
     • Naive Bayes
     • SVM
     • Decision Trees
3. Unsupervised Learning
   • • K-means
4. Neural Network
5. Types of Learning
   • • Supervised
       • • Linear Regression
         • Polynomial Regression
         • Binary Classification
         • Multi class Classification
         • Multi Label Classification
6. Applied ML
   • • Data splitting cross validation
     • Addressing Overfitting in ML: Regularization
     • Hyperparameter tuning:
     • Examples: SVM, Log Reg regularization
7. Data preprocessing
   • • Why cannot we use raw data as is?
     • Types of data normalization
     • Data Visualization: Introduction to pyplot
     • Exploratory Data Analysis
       • • • Checking Data format
           • Checking Missing Values
           • Checking feature correlation
           • Feature selection
8. Metrics
   • • Binary Classification: Accuracy, F1, Precision, Recall
     • Multi Class Classification
     • ROC curve, AUC
9. Kaggle: Way to always keep solving new problems
10. Final Project: Topic selected by student and submitted for review
11. Advanced: Deep Learning
    • • Computer Vision: Intro to CNN
      • NLP: RNN, LSTM , State-of-art Transformers
      • Fine-Tuning and Pretraining DL
      • Generative Networks: GANs, Vision Transformers, NLP GPT transformers