📘 Foundations of AI & Machine Learning
Learn the core concepts of AI and machine learning using Python, with hands-on projects to build a strong technical base.
AI
🕒 Duration: 8 Weeks
🎯 Prerequisite: Basic computer literacy
🛠️ Tools & Tech:
Python
Jupyter Notebooks / Google Colab
NumPy – Numerical operations
Pandas – Data manipulation
Matplotlib & Seaborn – Data visualization
Scikit-learn – Machine learning models
Joblib – Model saving & loading
Imbalanced-learn – Handling class imbalance (essential in real-world data)
📚 Topics Covered:
Introduction to AI, ML, and Data Science
What is AI/ML? Real-world use cases and trends
Role of data in decision-making
Python Basics for AI
Variables, functions, conditionals, loops
Data structures: Lists, dictionaries, sets
Working with libraries and pip installation
NumPy & Pandas
Arrays, indexing, reshaping (NumPy)
DataFrames, filtering, groupby, joins (Pandas)
Data Visualization with Matplotlib & Seaborn
Histograms, bar charts, boxplots, correlation heatmaps
Visualizing trends and patterns before modeling
Data Preprocessing & Feature Engineering
Handling missing values, encoding categorical data
Feature scaling, outlier detection
Addressing imbalanced datasets with Imbalanced-learn (SMOTE, undersampling)
Supervised Learning Algorithms (Scikit-learn)
Linear Regression
Logistic Regression
Decision Trees & Random Forest
Model training, prediction, and tuning
Unsupervised Learning
K-Means Clustering
PCA for dimensionality reduction
Model Evaluation & Deployment Basics
Accuracy, Precision, Recall, F1-score
Confusion matrix, ROC curve
Saving and loading models using Joblib
Mini Projects
Example 1: Student Performance Prediction
Example 2: Loan Eligibility Classification
Optional capstone: Real-world dataset + Imbalanced classification
✅ Outcome:
Participants will gain hands-on experience in building, evaluating, and saving ML models using real-world datasets — preparing them for internships or junior AI roles.