Machine Learning Essentials with Python

Overview

This Machine Learning course provides essential skills for developing and deploying machine learning models. You will learn key techniques in supervised learning, including linear and logistic regression, decision trees, bagging, and boosting. This online machine learning course covers key topics, including principal component analysis (PCA) for dimensionality reduction, hyperparameter tuning, and model performance evaluation. You will also gain hands-on experience in data preprocessing, exploratory data analysis (EDA), and ensuring model interpretability.You will apply these concepts by building a Loan Approval Prediction System using logistic regression. This project guides you through data preprocessing, binary classification, and model evaluation, equipping you with practical skills to create interpretable, data-driven models. By the end of the course, you’ll be ready to develop machine learning solutions that solve real-world business challenges and provide actionable insights.

Syllabus

Introduction to Machine Learning

Foundational concepts of Machine Learning, including types, applications, and a step-by-step breakdown of how ML models learn from data.

Supervised Learning - Linear Regression

Supervised Machine Learning - Introduction, Linear regression and its Pearson’s coefficient, Linear regression mathematically and coefficient of determination, Exploratory Data Analysis (EDA), Model analysis and squared errors, Descriptive analysis on the dataset, Analyse the distribution of the dependent column, Missing values imputation, Bivariate analysis, Building model using all information, Exploratory Data Analysis (EDA), Fluke correlation.

Supervised Learning - Logistic Regression

Overview of Logistic Regression as a classification technique, introduces the sigmoid function, and evaluates models using confusion matrix, precision, and recall with hands-on exercises.

Introduction to Ensemble Techniques

Decision Trees - Introduction, Decision Trees - Hands-on exercise, Ensemble methods, Bagging, Bagging - Hands-on exercise, Boosting, Types of Boosting, Adaboosting and Gradient Boosting - Hands-on exercise, Random Forest, Random Forest - Hands-on exercise.

Introduction to Unsupervised Learning

Overview of Unsupervised Learning fundamentals with a focus on clustering, including K-means, Clustering - Types and Distance, Clustering - Distance calculations

Principal Component Analysis

Principal Component Analysis, PCA for Dimensionality Reduction

Feature Engineering and Cross Validation

Cross validation concept and procedure, Implementing K-Fold Cross Validation, Some salient features of K-Fold, Bootstrap sampling concept and hands-on.