Learn Machine Learning the way professionals use it — no fluff, just solid concepts, real projects, and practical tools.

Duration: 4 to 6 months (Self-paced or instructor-led)

Ideal for: Beginners with basic Python knowledge who want to become job-ready ML experts

Module 1: Foundations of Machine Learning

We start with the “why” behind ML — and build up from zero.

Topics:

• What is Machine Learning? Real-life use cases
• ML vs AI vs Deep Learning: What’s the difference?
• Types of ML: Supervised, Unsupervised, Reinforcement
• Workflow of a Machine Learning Project
• How machines “learn” from data – A human-friendly breakdown

Module 2: Python for Machine Learning

Don’t worry if you’re not a Python pro — we’ve got you.

Topics:

• Essential Python concepts (variables, functions, loops)
• Numpy, Pandas – The power tools for data
• Data visualization using Matplotlib & Seaborn
• Hands-on: Analyze real datasets with simple code

Module 3: Data Preprocessing & Cleaning

Garbage in, garbage out — so let’s learn to clean and prepare data like a pro.

Topics:

• Handling missing values, duplicates, and outliers
• Feature scaling: Normalization vs Standardization
• Label encoding and One-hot encoding
• Train/Test split, Cross-validation
• Exploratory Data Analysis (EDA) to understand your data deeply

Module 4: Supervised Learning

Teach machines to predict and classify like humans — only faster.

Topics:

• Regression:
  • Linear Regression
  • Polynomial Regression
  • Evaluation Metrics: MAE, MSE, R²
• Classification:
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees & Random Forest
  • Support Vector Machines (SVM)
  • Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1

Module 5: Unsupervised Learning

Make sense of data even when there are no labels.

Topics:

• Clustering with K-Means
• Hierarchical Clustering
• DBSCAN
• Dimensionality Reduction with PCA
• Real-life Applications: Market segmentation, Image compression, etc.

Module 6: Model Tuning & Optimization

Even a good model needs tweaking — this is where accuracy gets boosted.

Topics:

• Hyperparameter Tuning with Grid Search & Random Search
• Feature Selection & Importance
• Cross-validation in-depth
• Avoiding Overfitting & Underfitting
• Ensemble Learning: Bagging, Boosting (XGBoost, AdaBoost)

Module 7: Introduction to Deep Learning

Now we go deeper — into neural networks and the brain of AI.

Topics:

• What is a Neural Network?
• Perceptron, Activation Functions
• Forward & Backward Propagation (don’t worry, we’ll simplify)
• Building Neural Nets with TensorFlow/Keras
• Deep Learning vs Traditional ML

Module 8: Real-World Projects (Step-by-Step)

Apply what you’ve learned to real industry problems.

Projects:

1. House Price Prediction (Regression)
2. Email Spam Detection (Classification)
3. Customer Segmentation (Clustering)
4. Movie Recommendation System
5. Handwritten Digit Recognition (Neural Network)

Module 9: End-to-End Deployment

Build it, host it, and make it usable by real users.

Topics:

• Model Saving with Pickle & Joblib
• Flask & Streamlit for Web Apps
• Creating interactive ML dashboards
• Hosting on Heroku or Render
• Building a portfolio-ready ML app (live demo)

Module 10: Ethics, Bias, and Responsible AI

Make sure your models are fair, explainable, and not doing harm.

Topics:

• What is bias in ML? Where does it come from?
• Explainable AI (XAI) using SHAP & LIME
• Fairness in predictions & transparency
• Real-world case studies on ethical AI

Tools & Libraries You’ll Use

• Python, Jupyter Notebook
• Numpy, Pandas, Scikit-learn
• Matplotlib, Seaborn
• TensorFlow, Keras
• Streamlit, Flask
• GitHub, Google Colab

Final Capstone Project + Certification

Time to shine. You’ll build your own complete ML system from scratch.