To give learners a clear understanding of the AI/ML landscape and spark curiosity about how intelligent systems are built and used in today’s world.

• What is Artificial Intelligence?
• Difference between AI, ML, and Deep Learning
• Real-life applications
• Types of AI: ANI, AGI, ASI
• Branches of AI (ML, NLP, Robotics, Vision)

To provide learners with a clear conceptual framework of how different ML approaches work and when to use each type in practical scenarios.

• Supervised, Unsupervised, and Reinforcement Learning
• Classification vs Regression
• Common ML use cases
• Introduction to datasets

To equip learners with the foundational Python skills needed for manipulating data and building ML models efficiently.

• Numpy and Pandas overview
• Data structures: arrays, dataframes
• Importing, reading, and inspecting data
• Simple data preprocessing (handling nulls, data types)

To enable learners to prepare high-quality datasets and visualize them effectively, forming a strong foundation for building accurate and interpretable ML models.

• Data cleaning
• Feature scaling (Normalization, Standardization)
• Label encoding / One-hot encoding
• Visualization tools: Matplotlib & Seaborn basics

To give learners hands-on experience with building and evaluating a simple predictive model using real-world data.

• Understanding regression
• Simple Linear Regression with Scikit-learn
• Train-test split
• Model evaluation: MSE, R² Score

To help learners build, train, and evaluate classification models, providing the foundation for solving real-world problems that require categorical predictions.

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Evaluation metrics: Accuracy, Confusion Matrix, Precision, Recall

To help learners build, train, and evaluate classification models, providing the foundation for solving real-world problems that require categorical predictions.

• K-Means Clustering
• Use cases of clustering
• Visualization with cluster plots

To equip learners with the skills to implement and fine-tune tree-based algorithms for solving real-world machine learning problems efficiently.

• What is a decision tree?
• Introduction to ensemble learning
• Implementing Decision Tree and Random Forest classifiers

To help learners build robust models that perform well on unseen data by applying proper evaluation techniques and understanding key modeling pitfalls.

• Overfitting vs Underfitting
• Cross-validation
• Bias-Variance tradeoff
• Saving/loading models using joblib or pickle

To give learners practical, end-to-end experience in building and presenting a machine learning solution, reinforcing their skills and boosting confidence for future projects or interviews.

• What is NLP?
• Tokenization, stop words, stemming
• Text classification example (sentiment analysis)

To instill a strong sense of ethical responsibility and equip learners with practical guidance to pursue careers in AI and Machine Learning with purpose and integrity.

• AI in real-world: safety, bias, and fairness
• Ethics in AI development
• Career options in AI/ML
• Building a learning roadmap

To reinforce practical skills, build project confidence, and prepare learners for future self-directed or professional work in AI and ML.

• Mini Project: Predicting house prices / Iris classification / Spam detection
• Presenting results with visualizations
• Final Q&A, feedback, and next steps

To familiarize learners with industry-standard tools and platforms, empowering them to practice, experiment, and build machine learning projects independently.

• Python with Jupyter Notebook / Google Colab
• Libraries: Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn
• Datasets: Iris, Titanic, House Prices (Kaggle/UCI)
• Practice Platforms: Kaggle, Google Colab, GitHub