class_01: Introduction to Machine Learning, Python Basics
class_02: Libraries -- Numpy, Matplotlib
class_03: KNN, Pandas, Generating Datasets
class_04: Face Recognition using kNN and Haar Cascade
class_05: KMeans Clustering (Lloyd's), Dominant Color Extraction and Image Segmentation
class_06: Decision Trees, Random Forests
class_07: Principal Component Analysis (PCA), PCA on MNIST
class_08: Univariate, Multivariate Linear Regression
class_09: Logistic Regression
class_10: Neural Networks Theory, Manifolds
class_11: NN from scratch using Numpy
class_12: Convolutional Neural Networks (CNN)
class_13: Autoencoders
class_14: Web Scraping, NLTK
class_15: Naive Bayes, Text Generation using Markov Chains
class_16: Word Embeddings (Word2Vec, GloVe), Gensim
class_17: Hands-on with CNN Projects
class_18: RNN (IMDB Sentiment Analysis), GRU (theory), LSTM (Seq2Seq; Text Gen)
class_19: Recommender Systems
class_20: Transfer Learning, [DC]GAN on MNIST, Neural Art
class_21: Overfitting, Genetic Algorithms, Intro to Reinforcement Learning