Course Overview
This comprehensive 110-hour course is designed for aspiring data scientists, software engineers, or AI enthusiasts who want to build strong foundational and practical skills in Artificial Intelligence and Machine Learning. The course blends theory with hands-on practice, covering essential tools like Python, Scikit-learn, TensorFlow, and real-world datasets. By the end, learners will be able to build, evaluate, and deploy their own ML models.
🎯 Learning Objectives
By the end of this course, learners will:
- Understand core AI/ML concepts and types
- Gain strong Python and data manipulation skills
- Build and evaluate supervised and unsupervised ML models
- Develop deep learning models using TensorFlow/Keras
- Apply ML to domains like computer vision and natural language processing
- Learn ethical AI practices and explainability techniques
- Deploy ML models to production using web frameworks
Prerequisites
Basic understanding of math (algebra, probability)
Logical reasoning and problem-solving skills
Prior programming experience is a plus (Python preferred)
A laptop with internet access
🧪 Tools & Technologies Used
Languages: Python
Libraries: Pandas, Numpy, Scikit-learn, TensorFlow, Keras, Matplotlib
Platforms: Jupyter, Google Colab, GitHub
Deployment: Flask, Streamlit
Visualization: Seaborn, Plotly
⏱️ Total Duration: 110 Hours
Format: 1-on-1 or group tutoring, hybrid learning with hands-on practice, quizzes, and a final capstone project.
🧩 Detailed Module Breakdown
📘 Module 1: Introduction to AI & Machine Learning (10 Hours)
Understand what AI and ML really are, their real-world applications, and how they differ from traditional programming.
- AI vs ML vs Deep Learning
- Supervised vs Unsupervised vs Reinforcement Learning
- ML pipeline overview
- Popular applications in health, finance, and robotics
- Environment setup (Python, Jupyter, Colab)
- Hands-on: Your first AI model on Google Colab
📊 Module 2: Python for Data Science & ML (10 Hours)
Build core Python skills tailored for ML, including data manipulation and visualization.
- Python basics: data types, loops, functions
- Working with NumPy arrays
- Dataframes using Pandas
- Data visualization using Matplotlib & Seaborn
- Hands-on: Analyze and visualize a dataset (e.g., COVID-19, Titanic)
🧠 Module 3: Supervised Learning Essentials (10 Hours)
Dive into regression and classification, the backbone of most real-world ML systems.
- Linear and Logistic Regression
- K-Nearest Neighbors (KNN)
- Evaluation metrics: accuracy, precision, recall, F1-score
- Hands-on: Predict housing prices, classify emails as spam/ham
🔍 Module 4: Advanced Supervised Learning (10 Hours)
Learn tree-based models and ensemble techniques used by top-performing models.
- Decision Trees, Random Forests
- Gradient Boosting (XGBoost, LightGBM)
- Model tuning: GridSearchCV
- Handling imbalance with SMOTE
- Hands-on: Customer churn prediction
🔎 Module 5: Unsupervised Learning Techniques (10 Hours)
Explore data without labels using clustering and dimensionality reduction.
- K-Means and DBSCAN clustering
- Principal Component Analysis (PCA)
- t-SNE for visualization
- Anomaly detection techniques
- Hands-on: Market segmentation project
🧰 Module 6: Model Evaluation & Tuning (10 Hours)
Enhance your models through fine-tuning, cross-validation, and feature engineering.
- Overfitting vs Underfitting
- Cross-validation techniques
- Feature importance & selection
- Hyperparameter tuning
- Hands-on: Improve a low-performing model step-by-step
🧠 Module 7: Neural Networks & Deep Learning (10 Hours)
Build your first deep neural networks and understand how they learn.
- Perceptron and Multi-layer Perceptrons (MLP)
- Activation functions (ReLU, Sigmoid, Softmax)
- Backpropagation algorithm
- TensorFlow/Keras basics
- Hands-on: Digit recognition using MNIST
🖼️ Module 8: Computer Vision & CNNs (10 Hours)
Learn to work with image data using powerful convolutional neural networks.
- Convolution layers, pooling, padding
- Transfer learning (VGG, ResNet)
- Image augmentation
- Hands-on: Real-time object detection or image classifier
🗣️ Module 9: Natural Language Processing (NLP) (10 Hours)
Teach machines to understand human language using NLP and sequence models.
- Text preprocessing (stopwords, stemming, tokenizing)
- TF-IDF and word embeddings
- RNN, LSTM basics
- Transformers and BERT overview
- Hands-on: Sentiment analysis / Chatbot prototype
⚖️ Module 10: Explainable & Ethical AI + Deployment (10 Hours)
Make your AI models understandable, ethical, and deployable.
- Explainable AI: SHAP, LIME
- AI Bias & Fairness
- Introduction to model deployment using Flask/Streamlit
- Hands-on: Build and deploy a simple ML model with UI
🚀 Module 11: Capstone Project (10 Hours)
Apply everything you’ve learned to a real-world project.
- Choose a problem (healthcare, finance, retail, etc.)
- Build an end-to-end AI/ML system
- Document code, visuals, model logic
- Deploy and present your model
- Deliverable: Final project + GitHub repo + video presentation
📚 What You Will Receive
- 11 Module Notes + Exercises
- GitHub Portfolio with Assignments & Capstone
- Access to tutor feedback & support
- Certificate of Completion (optional, if used in a formal program)