Machine Learning

About This Course

The Machine Learning course is a comprehensive, hands-on program designed to teach learners how to analyze data, build predictive models, and deploy machine learning solutions.

This course focuses on core ML concepts, supervised & unsupervised learning, model evaluation, and real-world projects using Python and popular ML libraries.
By the end of the course, students will be able to solve real-life problems using data-driven approaches.

What You Will Learn

After completing this course, students will be able to:

Understand machine learning concepts & workflows

Prepare and clean datasets

Apply supervised & unsupervised algorithms

Build predictive models using Python

Evaluate and optimize ML models

Work with real-world datasets

Implement basic ML pipelines

Create end-to-end ML projects

Prepare for ML interviews & roles

Requirements

Basic computer knowledge

Laptop or desktop computer (8GB RAM recommended)

Stable internet connection

Basic Python programming knowledge (recommended)

Basic understanding of mathematics (algebra & statistics – helpful but not mandatory)

Course Content

🔹 Module 1: Introduction to Machine Learning

What is Machine Learning?

Types of Machine Learning

Real-world applications

ML vs AI vs Deep Learning

ML workflow

🔹 Module 2: Python for Machine Learning

Python recap

NumPy fundamentals

Pandas for data handling

Data visualization with Matplotlib & Seaborn

🔹 Module 3: Mathematics & Statistics for ML

Linear algebra basics

Probability concepts

Mean, median, variance, standard deviation

Correlation & distributions

🔹 Module 4: Data Preprocessing

Data cleaning

Handling missing values

Feature scaling

Encoding categorical data

Train-test split

🔹 Module 5: Supervised Learning

Linear Regression

Logistic Regression

K-Nearest Neighbors (KNN)

Decision Trees

Random Forest

Model evaluation metrics

🔹 Module 6: Unsupervised Learning

Clustering concepts

K-Means clustering

Hierarchical clustering

Dimensionality reduction (PCA)

🔹 Module 7: Model Evaluation & Optimization

Overfitting & underfitting

Cross-validation

Hyperparameter tuning

Bias-variance tradeoff

🔹 Module 8: Machine Learning Libraries

Scikit-learn

Pipeline creation

Model persistence (pickle, joblib)

🔹 Module 9: Introduction to Deep Learning

What is Deep Learning?

Neural network basics

Introduction to TensorFlow & Keras

Use cases overview

🔹 Module 10: Projects & Case Studies

House price prediction

Customer churn prediction

Spam email detection

Recommendation system (basic)

Real-world dataset analysis