Machine Learning Course

Machine Learning Courses

A brief summary of the topics covered in this course is as below. This is 150 hours course, it is suggested to complete this course in 2 Months. Apart from my classroom course, you will be given exercises, and it will take another 100 hours in the course duration to complete these exercises.

Introduction to Machine Learning Supervised Learning

What is Machine Learning?
Supervised vs Unsupervised Learning
Type of ML problems
High Level view of ML Project Lifecycle

Linear Regression

Introduction to regression – equation, limitations
Types of regressions
Simple linear regression – Best-fit line, OLS, goodness of fit, Assumptions
Model building
Model Evaluation (regression parameters), Residual analysis and prediction, model interpretation
The Mathematics of regression (parameter estimation using OLS, the gradient descent algorithm, ANOVA)
Transformation of variables : Scaling and Standardization
Polynomial regression
Ordinary Least Squares
Linear Regression
Gradient Descent

Multiple linear regression

SLS vs MLR
Multicollinearity
Dummy Variable
Polynomial regression
Feature Selection
Model Building: BACKWARD, FORWARD, STEPWISE
R Square and Adjusted R Square
Loss: RMSE , MSE, MAE Comparison
Interpreting coefficients of MLR

Regularization

Introduction to Regularization
Regularized linear models
Ridge regression
Lasso regression
Elastic net

Classification

Introduction : Regression vs classification, types of classification, evaluating classification models
Logistic Regression : Best-fit sigmoid curve, odds & log odds, multivariate logistic regression
Building Logistic Regression Model
Model Evaluation: Confusion metrics and accuracy, sensitivity & specificity, precision & recall, trade-offs, ROC-AUC, predictions
Transformation of variables : Scaling and Standardization (optional)
Decision Trees : Descriptive vs Discriminative classification, the decision tree algorithm, measuring purity (Gini index, Entropy, Information gain),
Building Decision Trees Model
K-Nearest Neighbor Model
Telecom Churn Case Study

Ensamble Model

Introduction to Ensemble Modelling
Bagging (Bootstrap Aggregation) Model Introduction
Random Forest
Boosting Model Introduction
Adaboost, Gradient Boost, XGBoost, Light GBM, CatBoost
Stacking
Bledning
Out of Bag (OOB)
Feature importance in random forests
Building Random Forest Model
Building Boost Based Model

Support Vector Machine (SVM)

Linear SVM classification
Mathematical/ geometrical intuition
In-depth geometrical intuition
Soft margin classification
Nonlinear SVM classification
Polynomial kernel
Gaussian, RBF kernel
Data leakage
SVM Regression
Mathematical/ geometrical intuition

Naïve Bayesian

Introduction to Bayes theorem
Multinomial naïve Bayes
Gaussian naïve Bayes
Various type of Bayes theorem and their intuition

Clustering & Market Basket Analysis

Introduction to clustering, types of clustering, Euclidean distance & centroid
K-means clustering algorithm
Transformation of variables : Scaling and Standardization (Optional)
Building K-means model
Introduction to market basket analysis, cross-selling & upselling, bag vs basket of products, the Apriori algorithm,
Market Basket Analysis
Gaussian Mixture Model
K-Means
K-Means++
Batch K-Means
Hierarchical Clustering
DBSCAN
Evaluation of clustering
Homogeneity, completeness and v-measure
Silhouette coefficient
Davies-bouldin index
Contingency matrix
Confusion matrix

Model Evaluation & Model Selection

Principles of model selection – model & learning algorithm
Simplicity, Complexity & overfitting, bias-variance trade off.
Tuning Complexity and Regularization
Regularization, hyperparameters, and cross validation
Model building & Model evaluation
Hyperparameter tuning using grid-search and randomized-search CV
Handling class imbalance
Model Selection

Feature Engineering

Feature engineering – introduction
Handling numeric features, handling categorical features, handling time-based features
Feature selection using CV
Feature selection
Recursive feature elimination
Backward elimination
Forward elimination
Handling missing data
Handling outliers
Filter method
Wrapper method
Embedded methods
Feature scaling
Standardization
Mean normalization
Min-max scaling
Unit vector
Feature extraction
PCA (principle component analysis)
Introduction to Data encoding
Nominal encoding
One hot encoding
One hot encoding with multiple categories
Mean encoding
Ordinal encoding
Label encoding
Target guided ordinal encoding
Covariance
Correlation check
Pearson correlation coefficient
Spearman’s rank correlation
VIF

Handling Imbalance Data

Introduction to Data Imbalance
Up-sampling
Down-sampling
Undersampling using Tomek Links
K-Fold Cross Validation
Stratified K-Fold
Synthetic Minority Oversampling technique (SMOTE)
Adjusting Class Weight
Random Oversampling
Data interpolation
Choosing Right Evaluation Metric
Treat problem as Anomaly Detection

Model Evaluation Metrics

Confusion Matrix
Accuracy, Recall (Sensitivity/ TPR), Precision, F1, ROC, AUC
Error Rate, Specificity, FPR, Prevalence
RMSE, MAE, MSE
R Square, Adjusted R Square

Loss Function

Introduction to Regression and Classification Loss Function
Root Mean Square Error (RMSE)
Mean Square Error (MSE)
Mean Average Error (MAE)
Huber Loss
Maximum Likelihood Estimation
Binary Cross Entropy Loss
Hinge Loss
Multi Class Cross Entropy Loss
KL (Kullback Leibler) Divergence Loss

Model Monitoring

Introduction to model monitoring
Model Drifting
What to monitor?
How frequently evaluate?
How to take decision?

Model Retraining

Introduction to model retraining
Retraining on same algorithm and new data
Trying new features
Trying new algorithms

Dimensionality reduction

The curse of dimensionality
Dimensionality reduction technique
PCA (principle component analysis) Introduction & Maths
Scree plots
Eigen-decomposition approach
tNSE

Decision Trees Based ML

Decision Tree
Definition of Ensemble techniques
Bagging technique
Bootstrap aggregation
Random forest (bagging technique)
Random forest repressor
Random forest classifier
Complete end-to-end project with deployment
Adaboost, LGBM, XGBoost
Gradient Boost

Hari Thapliyaal