Course: Machine Learning and NLP Basics
Modules:
Course Introduction, Artificial Intelligence Essentials, Disciplines of AI, Various Application of AI Disciplines, Types of AI, Type-I of Artificial Intelligence, Type-II of Artificial Intelligence, Machine Learning Fundamentals, Applications of Machine Learning, Predictive ML Models, Classification and Other Models, ML Algorithms: Deep Dive, ML Algorithms - Part ll, Supervised Machine Learning, Applications of Supervised Learning, Market Segement Strategies of Unsupervised Machine Learning, Introduction to Unsupervised Machine Learning, Semi-supervised Learning, Reinforcement Learning, Use - Case of Reinforcement, Classification, Types of Classification Algorithm, Other types of Classification Algorithm, Demonstration on Classification, Feature Scailing and Training the Classifier, Visualization of Classification Report, Regression, Demonstration on Regression
Deep Learning Fundamentals, Machine Learning Vs. Deep Learning, Human Brain vs Neural Network, Introduction to Neural Network, Perceptron, Components of Perceptron, Learning Rate, Lower Learning Rate, Epoch, Importance of Epoch, Batch Size, Choosing the Right Batch Size, Single Layer Perceptron, Working of Single Layer Perceptron, Installing TensorFlow, TensorFlow Installation, Defining Sequence model layers, Activation Function, Advanced Activation Functions, Layer Types, Types of Layer Type, Model Compilation, Uses of Model Compilation, Model Optimizer, Understanding Model Optimizer, Uses of Model Optimizer, Digit Classification using Simple Neural Network in TensorFlow 2.x, Improving the model, Adding Hidden Layer, Hidden Layers in Neural Network, Adding Dropout, Adam Optimizer, How to use Adam Optimizer?, Image Classification Example, Image Classification - II, Convolutional Neural Network, Why is CNN Preferred over MLP, ReLU Layer, Pooling, Implementation of ReLU Layer, Data Flattening, Stacking up the Layers, Flattening Layer, Fully Connected Layer, The Final Layer, Predicting a cat or a dog, Model Building For Cat Vs. Dog Classification, Demonstration on Dog Vs Cat - I, Demonstration on Dog Vs Cat - II, Demonstration on Dog Vs Cat - III, Importance Of Saving And Loading A Model, Saving and Loading a Model, Demo-Saving and Loading the Model, Implementing RNN, LSTM Basics, LSTM Structure, Gate, Gates in LSTM, Input, Output and Forget Gate, LSTM Architecture, LSTM Architecture: Overview, LSTM Architecture: GATES, Importance of LSTM Architecture, Sequence Based Model, Sequence Based Model in CNN, Sequence Based Model in CNN: Continuation, Types of LSTM, Vanilla LSTM and Stacked LSTM, Convolutional Neural Network LSTM, Bi-Directional LSTM
Text Mining, Need of Text Mining, Applications of Text Mining, Comparison of Applications in Text Mining, Setting Up NLTK, Demonstration on Setting-up NLTK, Accessing the NLTK Corpora, Tokenization, Types of Tokenization, Uses of Tokenization, Bigrams, Trigrams & Ngrams, Demonstration on Bigrams, Trigrams and Ngrams, Stemming, Different types of Stemmer, Demonstration on Stemming, Lemmatization, Lemmatization Using NLTK, Stopwords, Demonstration on Stopwords, POS Tagging, Common Tags and Descriptions of POS, Need of POS Tags, Demonstration on Parts of Speech, Bag of Words, Demonstration on Bag of Words Approach, Demonstration on Bag of Words Approach - II, Text Processing, Count Vectorizer, Count Vectorization in Scikit - Learn, Term Frequency (TF), Term frequency in Scikit - Learn, Demonstration on Term Frequency, Demonstration on Term Frequency - II, Inverse Document Frequency (IDF), Inverse Document Frequency (IDF) Example, Multinomial Naive Bayes Classifier, Multinomial Naive Bayes Algorithm, Leveraging Confusion Matrix, Representation of Confusion Matrix
Course Summary