• Learning Objectives: Define AI, ML, and DL; Understand the relevance of AI for developers; Outline the typical ML workflow; Set up the development environment.
• Topics:
  • What is Artificial Intelligence (AI)? Machine Learning (ML)? Deep Learning (DL)? – History, Definitions, Key Differences.
  • Why AI/ML Matters for Software Developers: Use cases (recommendation systems, NLP, computer vision, predictive maintenance, etc.), market trends.
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning (high-level overview).
  • The Machine Learning Workflow: Problem Definition -> Data Collection -> Data Preprocessing -> Model Selection -> Training -> Evaluation -> Deployment -> Monitoring.
  • Setting Up Your Environment:
    • Python Installation (Anaconda recommended for package management).
    • Virtual Environments (conda or venv).
    • Essential Libraries Overview: NumPy, Pandas, Matplotlib, Scikit-learn.
    • IDE/Editor Setup (VS Code, Jupyter Notebooks/Lab).
• Hands-on Lab: Environment setup, running simple Python scripts, introduction to Jupyter Notebooks.

• Learning Objectives: Master basic NumPy and Pandas operations; Perform data loading, cleaning, and visualization; Understand the importance of data quality.
• Topics:
  • NumPy Fundamentals: Arrays, indexing, mathematical operations, broadcasting.
  • Pandas Fundamentals: Series, DataFrames, data loading (CSV, JSON, basic SQL), inspection (.head(), .info(), .describe()), selection & filtering (loc, iloc).
  • Data Cleaning: Handling missing values (dropping, imputation), identifying duplicates.
  • Basic Data Visualization: Utilizing Matplotlib and Seaborn for understanding data distributions and relationships, including histograms, scatter plots, and box plots.
  • Introduction to Feature Engineering: Creating new features from existing ones (brief overview).
• Hands-on Lab: Loading a dataset, cleaning missing values, performing exploratory data analysis (EDA) with Pandas and Matplotlib/Seaborn.

• Learning Objectives: Understand regression problems, implement linear regression, evaluate model performance, and grasp core concepts such as training/testing splits and overfitting.
• Topics:
  • Concept of Supervised Learning: Learning from labeled data.
  • Regression Problems: Predicting continuous values.
  • Linear Regression: Concepts, Assumptions, and Implementation Using Scikit-learn.
  • Model Evaluation for Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared.
  • Data Splitting: The Importance of Train, Validation, and Test Sets.
  • Underfitting vs. Overfitting: Intuition behind the Bias-Variance Tradeoff.

Hands-on Lab: Build and evaluate a linear regression model on a sample dataset (e.g., predicting house prices, Boston Housing). Experiment with train/test splits.

• Learning Objectives: Understand classification problems, implement common classification algorithms, and evaluate classifiers using appropriate metrics.
• Topics:
  • Classification Problems: Predicting discrete categories.
  • Common Algorithms:
    • Logistic Regression (for binary classification).
    • K-Nearest Neighbors (KNN).
    • Support Vector Machines (SVM – basic concepts).
    • Decision Trees & Random Forests (intuition and usage).
  • Model Evaluation for Classification: Accuracy, Confusion Matrix, Precision, Recall, F1-score, ROC Curve & AUC.
  • Handling Categorical Features: One-Hot Encoding, Label Encoding.
  • Feature Scaling: Standardization, Normalization (why and when).

Hands-on Lab: Build and compare multiple classifiers (e.g., Logistic Regression, KNN, Random Forest) on a dataset (e.g., Iris dataset, Titanic survival prediction). Practice feature encoding and scaling. Evaluate using various metrics.

• Learning Objectives: Understand the goals of unsupervised learning; Apply clustering and dimensionality reduction techniques.
• Topics:
  • Concept of Unsupervised Learning: Finding patterns in unlabeled data.
  • Clustering: Grouping similar data points.
    • K-Means Algorithm: How it works, choosing ‘k’, implementation.
    • Evaluation: Silhouette Score (intuition).
  • Dimensionality Reduction: Reducing the number of features.
    • Principal Component Analysis (PCA): Concept, use cases (visualization, noise reduction, speeding up supervised learning).
• Hands-on Lab: Apply K-Means clustering to a dataset and visualize the results (using PCA if needed). Apply PCA for dimensionality reduction and observe its effect.

• Learning Objectives: Understand the fundamental concepts of neural networks, construct a simple neural network using a high-level library, and identify practical use cases for CNNs and RNNs.
• Topics:
  • Limitations of Traditional ML.
  • The Neuron and Neural Network Basics: Layers, Activation Functions (ReLU, Sigmoid, Tanh), Forward Propagation (brief overview).
  • Introduction to Deep Learning Frameworks: TensorFlow with Keras API or PyTorch (choose one or provide a high-level comparison).
  • Building a Simple Feedforward Neural Network for Classification and Regression.
  • Brief Introduction to Convolutional Neural Networks (CNNs): Concepts (Convolution, Pooling), Use Cases (Image Recognition).
  • Brief Introduction to Recurrent Neural Networks (RNNs/LSTMs): Concepts (Handling Sequences), Use Cases (Text analysis, Time Series).
• Hands-on Lab: Build a simple Dense Neural Network using Keras/PyTorch for a classification task (e.g., MNIST digit recognition or re-visiting a previous classification problem). Optional: Use a pre-trained CNN for a simple image classification task.

• Learning Objectives: Understand the basics of model deployment, be aware of MLOps concepts, recognize ethical implications, and know where to learn more.
• Topics:
  • Saving and Loading Models: Utilizing joblib, pickle, or framework-specific methods.
  • Basic Model Deployment Strategies:
    • Wrapping a model in a simple API (e.g., using Flask/FastAPI).
    • Batch prediction.
  • Introduction to MLOps: Why is it crucial? (Version control for data/models, monitoring, CI/CD for ML).
  • AI Ethics and Bias: Fairness, Accountability, Transparency, and Potential Pitfalls.
  • Limitations of AI/ML.
  • Resources for Further Learning: Key blogs, communities (e.g., Kaggle), documentation, and additional specializations (e.g., NLP, CV, Reinforcement Learning, etc.).
• Hands-on Lab: Save a trained Scikit-learn model. Build a very simple Flask API endpoint that takes input data and returns a prediction using the loaded model.

Discussion: Ethical Scenarios and Considerations in AI Development.

• Goal: Apply learned concepts to a small, end-to-end project.
• Ideas:
  • Develop a classifier tailored to a specific domain (e.g., spam detection, sentiment analysis on simple text).
  • Develop a regression model for a predictive task (e.g., predicting user engagement or sales forecast).
  • Explore a public dataset (Kaggle, UCI Repository) and perform EDA, modeling, and evaluation.

Deliverable: Jupyter Notebook detailing the process, code, results, and interpretation. Optionally, a simple API wrapper.

• Real datasets
• Code-along labs and mini-projects
• Model building & deployment labs

Peer programming and Q&A sessions

✅ Resume & LinkedIn Profile Review
✅ Mock Interviews & Job Assistance
✅ Lifetime Access to Course Materials
✅ One-on-One Doubt Clearing Sessions