1. Supervised Learning methods: linear & logistic regressions, SVM, kNN, decision trees
2. AI & ML API: Google / Amazon / Microsoft ML APIs
3. Unsupervised Learning methods for Clustering
4. AI / ML / DL frameworks & libraries - TensorFlow
5. Deep Learning methods: Neural Networks (NN), Convolutional NN (CNN),
Recurrent NN (RNN), Long-Short Term Memory (LSTM), Auto-encoders, Time-series analysis
6. Reinforcement Learning & Transfer Learning techniques: Discover how to
transfer your model from a domain to another similar data domain
7. Mini-project Customer Segmentation (e-Commerce)
8. Mini-project Fraud detection (Banking)
MODULE 2
Machine Learning
& Deep Learning techniques