Machine Learning (Perceptron) class

class_01: Introduction to Machine Learning, Python Basics
class_02: Numpy, Matplotlib, Statistics
class_03: Pandas, KNN, KNN on MNIST
class_04: OpenCV, Face Detection using Haar Cascade, Face Recognition
class_05: KMeans, Dominant Color Extraction and Image Segmentation
class_06: Decision Trees, Random Forest
class_07: Principal Component Analysis (PCA)
class_08: [Uni, Multi]Variate Linear Regression
class_09: Logistic Regression
class_10: Neural Network Theory
class_11: NN from scratch, Keras Sequential on MNIST
class_12: Convolutional Neural Networks (CNN)
class_13: [Stacked, Convolutional] AutoEncoders, Keras Functional API
class_14: NLTK, Web Scraping
class_15: Naive Bayes, Markov Chains (Text Generation)
class_16: Word2Vec with Gensim, Train Word Embeddings (bi-gram context)
class_17: RNN, LSTM, GRU
class_18: Transfer Learning, Deep Dream, Neural Art
class_19: Seq2Seq, LSTM Applications
class_20: Genetic Algorithms, Intro to RL

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