AI & Machine Learning: Build Your ML Models!

Learn regression, classification, clustering, pipelines & neural networks with hands-on mini projects.

📅8 Weeks
⏰2-3 hrs/week
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Who This Course Is For

  • • Beginners with basic Python knowledge
  • • Students, professionals, and self-learners looking to enter ML
  • • No prior math or ML experience required

Learning Outcomes

  • • Understand core ML concepts (regression, classification, clustering)
  • • Learn key math foundations (linear algebra, statistics)
  • • Build ML models with Scikit-learn & basic neural nets with Keras
  • • Complete real-world mini projects
CURRICULUM

What You'll Learn

Chapter 1: Introduction to Machine Learning & Python for ML

Module 1.1: Foundations of AI & ML
  • •ML vs AI vs DL
  • •Types of ML: Supervised, Unsupervised, Reinforcement
  • •Real-world applications of ML
Module 1.2: Python & ML Libraries Refresher
  • •Working in Jupyter Notebooks
  • •Variables, functions, control flow
  • •Importing and using: numpy, pandas, matplotlib, seaborn
Module 1.3: Exploratory Data Analysis (EDA)
  • •Loading datasets with pandas
  • •Handling missing values
  • •Visualizations: bar plots, histograms, boxplots
Mini Project: Titanic Dataset – EDA Challenge
  • •Clean the dataset
  • •Explore passenger survival patterns
  • •Create basic visualizations

Chapter 2: Math for ML – Linear Algebra & Statistics

Module 2.1: Linear Algebra for ML
  • •Vectors and matrices
  • •Dot product, matrix multiplication
  • •Matrix operations in NumPy
Module 2.2: Descriptive Statistics & Probability
  • •Mean, median, mode, variance
  • •Probability distributions (Normal, Binomial)
  • •Z-score, standard deviation
Module 2.3: Visualizing Math in Python
  • •Plotting distributions with seaborn
  • •Boxplots and violin plots
  • •Covariance matrices
Mini Project: Student Scores Dataset – Stats Dashboard
  • •Summarize and visualize score distributions
  • •Highlight high-performing students and outliers

Chapter 3: Regression & Evaluation Metrics

Module 3.1: Understanding Regression
  • •What is regression?
  • •Line of best fit & error metrics
  • •Use cases for regression
Module 3.2: Linear Regression in Scikit-learn
  • •train_test_split()
  • •Fitting a LinearRegression() model
  • •Making predictions
Module 3.3: Evaluating Regression Models
  • •MAE, MSE, RMSE
  • •R² Score
  • •Residual plots
Mini Project: Boston Housing Price Prediction
  • •Predict house prices using linear regression
  • •Analyze prediction accuracy using MSE & R²

Chapter 4: Classification + Overfitting

Module 4.1: Logistic Regression
  • •Binary classification intro
  • •Sigmoid function
  • •Thresholding and decision boundary
Module 4.2: Classification Metrics
  • •Confusion Matrix
  • •Accuracy, Precision, Recall, F1 Score
  • •ROC Curve and AUC
Module 4.3: Overfitting & Model Validation
  • •Underfitting vs Overfitting
  • •Train/test vs cross-validation
  • •Bias-variance tradeoff
Mini Project: Breast Cancer Classification
  • •Predict benign vs malignant tumors
  • •Evaluate model performance using multiple metrics

Chapter 5: Unsupervised Learning & PCA

Module 5.1: K-Means Clustering
  • •Euclidean distance & centroids
  • •Elbow method for selecting K
  • •Interpreting clusters
Module 5.2: PCA for Dimensionality Reduction
  • •Variance and Principal Components
  • •Transforming data to fewer dimensions
  • •When to use PCA
Module 5.3: Visualizing Unlabeled Data
  • •Scatter plots of cluster assignments
  • •2D PCA projections
Mini Project: Customer Segmentation using K-Means
  • •Cluster customers based on income/spending
  • •Reduce dimensions using PCA
  • •Visualize segments

Chapter 6: Full ML Workflow + Pipelines

Module 6.1: Data Preprocessing
  • •Feature scaling: StandardScaler, MinMaxScaler
  • •Encoding categorical variables: LabelEncoder, OneHotEncoder
Module 6.2: Feature Engineering Basics
  • •Creating new features
  • •Binning, encoding, interaction terms
  • •Handling skewed distributions
Module 6.3: Building Pipelines in Scikit-learn
  • •Introduction to Pipeline() and ColumnTransformer()
  • •Reusable end-to-end preprocessing
  • •Model chaining with pipeline
Mini Project: Titanic – End-to-End ML Pipeline
  • •Use pipeline to preprocess, train, and evaluate
  • •Reuse same pipeline on test set

Chapter 7: Neural Networks – Foundations

Module 7.1: What is a Neural Network?
  • •Perceptrons and layers
  • •Activation functions: sigmoid, relu, softmax
  • •Forward propagation
Module 7.2: Implementing with TensorFlow/Keras
  • •Sequential() API
  • •Compiling and training models
  • •Evaluating model accuracy
Module 7.3: Visualizing Model Performance
  • •Accuracy/loss curves
  • •Visualizing predictions
Mini Project: MNIST Handwritten Digit Classification
  • •Build and train a neural net using Keras
  • •Visualize misclassified digits

Chapter 8: Final Capstone Project

Module 8.1: Project Planning & Dataset Selection
  • •Choose a dataset (Kaggle/UCI)
  • •Define a business or real-world goal
  • •Plan pipeline (preprocessing → model → evaluation)
Module 8.2: Build & Train Model
  • •Perform EDA
  • •Clean, preprocess, scale
  • •Train and evaluate multiple models
Module 8.3: Deployment (Optional)
  • •Create dashboard using Streamlit
  • •Host on GitHub or share via Colab
Capstone Projects (Choose One)
  • •Iris Species Classification
  • •Diabetes Prediction
  • •Startup Profit Estimator
  • •Movie Ratings Prediction
  • •Final Deliverable: Well-documented notebook or app + GitHub repo
SUCCESS STORIES

What Our Students Say

PV
"Before this program I only knew basic Python. In 8 weeks I built ML models, learned pipelines and even a Keras network. The mini-projects made my portfolio shine!"
- Priya Verma
Data Science Intern @ AI Labs

Ready to Start Your ML Journey?

Join our next cohort and build your own ML models from scratch using Python, Scikit-learn and Keras.

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