➔ Module 1 : Introduction to Machine Learning and Data Preprocessing  
     – What is machine learning?, How Machine Learning works? Applications of machine learning, Different types of machine learning, How do we know machines are learning right?, Different stages of machine learning projects.
     – Handling Numeric Features, Feature Scaling, Standardization and Normalization, Handling Categorical Features, One Hot Encoding, pandas get_dummies.
     – Label Encoding, More on different encoding techniques, Simple Train and Test Split, Drawbacks of train and test split, K-fold cross validation, Time based splitting, What is overfitting?, What causes overfitting?, What causes underfitting?, What are bias and variance?, How to overcome overfitting and underfitting problems?.

➔ Module 2: Regression Techniques in ML
     – Regression, Introduction to Linear Regression, Understanding How Linear Regression Works, Maths behind Linear Regression.
     – Ordinary Least Square, Gradient Descent, R-square, Adjusted R-square, Polynomial Regression, Multiple Regression.
     – Performance Measures – MSE, RMSE, MAE, Assumption of Linear Regression, Ridge and Lasso regression, RFE (Recursive Feature Elimination).

➔ Module 3 : Classification Algorithms in ML 
     – Logistic regression, Introduction to classification problems, Introduction to logistic regression, Why the name regression?.
     – The sigmoid function, Log odds, Cost function, Feature importance and model interpretability, Collinearity of features, Feature engineering for non-linearly separable data.
     – ORDER BY clause in SQL, SORT result by column, SORT by alias, expressions, or column numbers, LIMIT clause in SQL, How to limit the number of rows, How to return the range of rows.Performance Metrics for Classification Algorithms, Accuracy Score, Confusion Matrix, TPR, FPR, FNR, TNR, Precision – Recall, F1-Score, ROC Curve and AUC, Log Loss, K Nearest Neighbors.
     – Introduction to KNN, Effectiveness of KNN, Distance Metrics, Accuracy of KNN, Effect of outlier on KNN, Finding the k Value, KNN on regression, Where not to use KNN, Natural Language Processing.
     – Introduction to NLP, Converting Text to vector, Data Cleaning, Preprocessing Text Data – Stop word removal, Stemming, Tokenization.
     – Lemmatization, Collecting Data from the web, Developing a Classifier, Building Pipelines for NLP projects, Uni-grams, bi-grams and n-grams, tf-idf, Word2Vec, Naive Bayes, Refresher on conditional Probability, Bayes Theorem, Examples on Bayes theorem, Exercise problems on Naive Bayes, Naive Bayes Algorithm, Assumptions of Naive Bayes Algorithm, Laplace Smoothing, Naive Bayes for Multiclass classification, Handling numeric features using Naive Bayes, Measuring performance of Naive Bayes, Support Vector Machines.
     – Introduction to SVM, What are hyperplanes?, Geometric intuition, Maths behind SVM, Loss Function, Kernel trick, Polynomial kernel, rbf and linear kernels, SVM Regression, Tuning the parameter, GridSearch and RandomizedSearch, SVM Regression, Decision Tree.
     – Introduction to Decision Tree, Homogeneity and Entropy, Gini Index, Information Gain, Advantages of Decision Tree, Preventing Overfitting, Advantages and Disadvantages, Plotting Decision Trees, Plotting feature importance, Regression using Decision Trees, Ensemble Learning.
     – Introduction to Ensemble Learning, Bagging (Bootstrap Aggregation), Constructing random forests, Runtime, Case study on Bagging, Tuning hyperparameters of random forest (GridSearch, RandomizedSearch), Measuring model performance, Boosting, Gradient Boosting, Adaboost and XGBoost, Case study on boosting trees, Hyperparameter tuning, Evaluating performance, Stacking Models.

➔ Module 4: Time Series and Unsupervised Learning
     – Time Series, Time Series Basics, Time Series Data Loading and Visualization, Feature Engineering on time series data, Resampling techniques on Time Series Data, Time Series Transformation, Power Transformation, Moving Averages, Exponential Smoothing, White Noise, Random Walk, Decomposing Time Series, Differencing, Splitting time series data, Naive (Persistence) Model, Auto Regression Model (AR), Moving Average Model (MA), ARIMA, SARIMA, Stationary time series, Linear Regression and Model Creation, Un-Supervised Machine Learning Algorithms, Clustering.
     – Introduction to unsupervised learning, Applications of Unsupervised Learning, Kmeans Geometric intuition, Maths Behind Kmeans, Kmeans in presence of outliers, Kmeans random initialization problem, Kmeans++, Determining the right k, Evaluation metrics for Kmeans.
     – Case study on Kmeans, Hierarchical Clustering, Agglomerative and Divisive, Dendograms.
     – Case study on hierarchical clustering, Segmentation, Case Study on Segmentation, DBSCAN       – Density based clustering, MinPts and Eps, Core Border and Noise Points, Advantages and Limitations of DBSCAN.
     – Case Study on DBSCAN clustering, What are the dimensions?, Why is high dimensionality a problem?     
      – Introduction to MNIST dataset with (784 Dimensions), Intro to Dimensionality reduction techniques, PCA (Principal Component Analysis) for dimensionality reduction, t-SNE (t-distributed stochastic neighbor embedding).

➔ Module 5: Recommender Systems and Deep Learning

     – Introduction to Recommender Systems, Recommender Engine Architecture, Content Based Recommendation, Cosine Similarity, K-Nearest Neighbors and content Recommendation, Neighborhood based collaborative filtering, User-based collaborative filtering, Item Based Collaborative filtering, Tuning Collaborative filtering Algorithms.
     – Matrix Factorization methods – PCA, SVD, NMF, Train/Test cross-validation, Evaluation metrics for recommender systems.
     – History of Deep Learning, Perceptrons, Multi-Level Perceptrons, Representations, Training Neural Networks, Activation Functions, Artificial Neural Networks.
     – Introduction, Deep Learning, Understanding Human Brain, In Depth Perceptrons, Example for perceptron, Multi Classifier Problems, Neural Networks, Input Layer, Output Layer, Sigmoid Function.
     – Introduction to TensorFlow and Keras, Training Neural Network, Understanding Notations, Activation Functions, Hyperparameter tuning in Keras, Feed Forward Networks, Online offline mode, Bidirectional RNN.
     – Understanding Dimensions, Back Propagation, Loss function, SGD, Regularization, Training for batches.
     – Mini Projects – Churn Prediction, Air Pollution Prediction, Books Recommender System, Dog Breed Classification, Loan Default Prediction.
     – Final Project – Building a Recommender System on Movie Lens Dataset/Toxic Comment Classification using NLP/ House Price Prediction.