Machine Learning Mastery (Weekend Program)

Course Modules & Weekly Breakdown

Week 1 – Introduction to Machine Learning & Python Setup

What is Machine Learning? Real-world applications

Installing Python, Jupyter Notebook, and libraries

Python basics: variables, loops, functions

Learning Outcome:

By the end of this week, students will understand the basic idea of machine learning, its real-world applications, and set up their Python environment. They will also gain confidence in writing simple Python programs using variables, loops, and functions.

Week 2 – Working with Data (NumPy & Pandas Basics)

Introduction to NumPy arrays & operations

Pandas DataFrames: loading, exploring, cleaning datasets

Handling missing values

Learning Outcome:

Students will be able to work with structured data by creating and manipulating arrays with NumPy and DataFrames with Pandas. They will also learn how to clean data and handle missing values effectively.

Week 3 – Data Visualization

Matplotlib basics: line, bar, scatter plots

Seaborn introduction for advanced visualization

Plotting dataset trends & correlations

Learning Outcome:

This week ensures students can create meaningful charts and graphs using Matplotlib and Seaborn. They will learn to visualize dataset trends, identify patterns, and build strong foundations in data-driven storytelling.

Week 4 – Introduction to Machine Learning Concepts

Types of ML: Supervised, Unsupervised, Reinforcement

Features & labels

Train/Test split concept with Scikit-learn

Learning Outcome:

By the end of this week, students will clearly understand the different types of machine learning—supervised, unsupervised, and reinforcement learning—and the key concepts of features, labels, and dataset splitting for model training.

Week 5 – Regression Models

Linear Regression (theory + implementation in Scikit-learn)

Multiple regression examples

Evaluating regression models (MSE, R² score)

Learning Outcome:

Students will implement linear and multiple regression models using Scikit-learn and evaluate them using metrics like MSE and R² score. They will also understand how regression helps in predicting continuous values.

Week 6 – Classification Models

Logistic Regression (binary classification)

K-Nearest Neighbors (KNN)

Evaluation metrics: Accuracy, Precision, Recall, F1 Score

Learning Outcome:

At the end of this week, students will build classification models with logistic regression and KNN. They will also learn how to assess classification performance using accuracy, precision, recall, and F1 scores.

Week 7 – Decision Trees & Random Forests

Decision Trees (theory & implementation)

Overfitting & pruning

Random Forest introduction

Learning Outcome:

Students will gain practical experience in building decision tree models and understanding the problem of overfitting. They will also explore random forests and see how ensemble methods improve predictions.

Week 8 – Unsupervised Learning

Clustering: K-Means algorithm

Dimensionality reduction: PCA (Principal Component Analysis)

Real-world examples (customer segmentation, image compression)

Learning Outcome:

By the end of this week, students will understand clustering with K-Means and dimensionality reduction using PCA. They will apply these techniques to real-world examples like customer segmentation and image compression.

Week 9 – Model Evaluation & Improvement

Cross-validation techniques

Bias vs variance tradeoff

Feature scaling & normalization

Learning Outcome:

Students will develop skills to evaluate models with cross-validation and understand the bias-variance tradeoff. They will also practice scaling and normalizing data to improve algorithm performance.

Week 10 – Intro to Neural Networks (Basics)

What are neural networks?

Perceptron model

Hands-on: Using Scikit-learn’s MLPClassifier

Learning Outcome:

This week helps students grasp the fundamental idea of neural networks, understand perceptrons, and implement a simple neural network using Scikit-learn’s MLPClassifier.

Week 11 – End-to-End ML Project (Guided)

Choose a dataset (Iris, Titanic survival, House prices, etc.)

Data preprocessing → Training → Evaluation

Visualization of results

Learning Outcome:

Students will work on a guided project where they select a dataset, preprocess it, train a model, evaluate the results, and present findings through visualizations.

Week 12 – Final Project & Career Guidance

Students build & present their own ML project

Tips for creating a GitHub portfolio

Career paths: Data Scientist, ML Engineer, AI Researcher

Freelancing & job opportunities

Learning Outcome:

By the end of the course, students will complete and present their own ML project, upload it to GitHub as part of their portfolio, and receive guidance on career paths, freelancing opportunities, and industry applications of ML.

Industry Trends Covered

• Growth of Machine Learning in industries like finance, healthcare, e-commerce, and education.
• Increasing demand for AI-powered automation and decision-making.
• Use of ML in real-world applications such as fraud detection, recommendation systems, and medical diagnostics.
• Rise of cloud-based ML platforms (AWS, Google Cloud, Azure).
• Expansion of open-source ML tools and libraries for faster development.
• High demand for skilled professionals in Data Science, AI Engineering, and ML freelancing.

Who Should Attend?

This course is ideal for:

• Beginners who want to enter the field of Machine Learning and AI
• Students & Graduates from Computer Science, Engineering, or related fields
• Aspiring Data Scientists who want hands-on exposure to Python ML libraries
• Working Professionals aiming to upskill in Machine Learning for career growth
• Freelancers interested in adding ML-based services to their skill set

Tools & Technologies You’ll Master

• Python 3.x – Core programming language
• Jupyter Notebook – Interactive coding & experimentation
• NumPy – Numerical computing & array operations
• Pandas – Data analysis & preprocessing
• Matplotlib & Seaborn – Data visualization & insights
• Scikit-learn – Core ML algorithms & evaluation
• GitHub – Portfolio building & version control

Learning Outcomes

By the end of this course, you will:

• Write Python programs and use NumPy & Pandas for data handling
• Perform data cleaning, preprocessing, and visualization for datasets
• Implement Supervised & Unsupervised ML algorithms (Regression, Classification, Clustering, PCA)
• Train, evaluate, and improve ML models using Scikit-learn
• Build a complete ML project from scratch and showcase it on GitHub
• Explore career opportunities in Data Science, ML Engineering, and AI freelancing