Artificial Intelligence & Machine Learning: 5-Day Intensive Training Course

Course Overview

This comprehensive 5-day course provides hands-on training in AI and Machine Learning fundamentals, covering the latest 2025 trends including Generative AI, autonomous agents, and ethical AI implementation. Designed for professionals seeking to master AI/ML concepts with practical applications.

📅 Day 1: Introduction to AI & Machine Learning Fundamentals

Morning Session (9:00 AM - 12:30 PM)

Module 1.1: Understanding Artificial Intelligence

• What is AI? Definition, history, and evolution

• Types of AI: Narrow AI, General AI, and Super AI

• AI vs ML vs Deep Learning: Key distinctions

• Real-world AI applications across industries (healthcare, finance, retail, manufacturing)

• 2025 AI Trends: Generative AI, autonomous systems, and multimodal AI

Module 1.2: Machine Learning Basics

• Introduction to Machine Learning concepts

• Types of Machine Learning:

  • Supervised Learning

  • Unsupervised Learning

  • Semi-supervised Learning

  • Reinforcement Learning

• ML workflow: Data collection, preprocessing, training, evaluation, deployment

• Hands-on Exercise: Setting up your ML development environment (Python, Jupyter, libraries)

Afternoon Session (2:00 PM - 5:30 PM)

Module 1.3: Python for AI/ML

• Python essentials for data science

• Key libraries: NumPy, Pandas, Matplotlib, Seaborn

• Data manipulation and visualization techniques

• Lab Work: Data exploration with real datasets

Module 1.4: Mathematics for Machine Learning

• Linear algebra fundamentals (vectors, matrices, operations)

• Calculus basics (derivatives, gradients)

• Statistics and probability for ML

• Practical Session: Mathematical foundations applied to ML problems

📅 Day 2: Supervised Learning & Classical ML Algorithms

Morning Session (9:00 AM - 12:30 PM)

Module 2.1: Regression Algorithms

• Linear Regression: Theory and implementation

• Multiple Linear Regression

• Polynomial Regression

• Ridge and Lasso Regression (Regularization techniques)

• Model evaluation metrics: MSE, RMSE, R², MAE

• Hands-on Project: House price prediction model

Module 2.2: Classification Algorithms

• Logistic Regression

• Decision Trees and Random Forests

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Evaluation metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC

Afternoon Session (2:00 PM - 5:30 PM)

Module 2.3: Advanced Classification Techniques

• Ensemble methods: Bagging, Boosting, Stacking

• Gradient Boosting: XGBoost, LightGBM, CatBoost

• Handling imbalanced datasets

• Cross-validation and hyperparameter tuning

• Lab Project: Customer churn prediction with ensemble methods

Module 2.4: Model Optimization

• Feature engineering and selection

• Grid Search and Random Search

• Automated Machine Learning (AutoML) - 2025 trend

• Model interpretability basics

📅 Day 3: Deep Learning & Neural Networks

Morning Session (9:00 AM - 12:30 PM)

Module 3.1: Introduction to Deep Learning

• Neural networks fundamentals

• Perceptrons and Multi-Layer Perceptrons (MLP)

• Activation functions: ReLU, Sigmoid, Tanh, Softmax

• Backpropagation and gradient descent

• Introduction to TensorFlow and PyTorch

Module 3.2: Convolutional Neural Networks (CNN)

• CNN architecture and components

• Convolution, pooling, and fully connected layers

• Popular CNN architectures: VGG, ResNet, Inception

• Transfer learning and pre-trained models

• Hands-on Project: Image classification with CNNs

Afternoon Session (2:00 PM - 5:30 PM)

Module 3.3: Recurrent Neural Networks (RNN) & NLP

• RNN architecture and applications

• LSTM and GRU networks

• Natural Language Processing (NLP) fundamentals

• Text preprocessing and tokenization

• Word embeddings: Word2Vec, GloVe

• Practical Exercise: Sentiment analysis project

Module 3.4: Transformers & Large Language Models

• Transformer architecture (Attention mechanism)

• BERT, GPT models overview

• Large Language Models (LLMs) - 2025 focus

• Introduction to prompt engineering

• Demo: Working with pre-trained language models

📅 Day 4: Generative AI & Advanced Topics

Morning Session (9:00 AM - 12:30 PM)

Module 4.1: Generative AI Revolution

• What is Generative AI? Current landscape 2025

• Generative Adversarial Networks (GANs)

• Variational Autoencoders (VAEs)

• Diffusion Models: Stable Diffusion, DALL-E

• Text-to-image, text-to-video generation

• Hands-on: Creating images with generative AI tools

Module 4.2: Large Language Models in Practice

• ChatGPT, Claude, Gemini: Understanding capabilities

• Fine-tuning LLMs for specific tasks

• Retrieval-Augmented Generation (RAG)

• Vector databases and embeddings

• Building AI-powered chatbots

• Project: Creating a custom AI assistant

Afternoon Session (2:00 PM - 5:30 PM)

Module 4.3: Computer Vision Applications

• Object detection: YOLO, R-CNN

• Image segmentation techniques

• Facial recognition systems

• Real-time video analysis

• Case Study: Autonomous vehicle vision systems

Module 4.4: Reinforcement Learning

• RL fundamentals and terminology

• Q-Learning and Deep Q-Networks (DQN)

• Policy gradients

• Autonomous agents - 2025 trend

• Applications in robotics and gaming

• Demo: Training an RL agent

📅 Day 5: ML Operations, Ethics & Real-World Deployment

Morning Session (9:00 AM - 12:30 PM)

Module 5.1: MLOps & Model Deployment

• MLOps fundamentals - Essential 2025 skill

• Model versioning and experiment tracking (MLflow, Weights & Biases)

• CI/CD for machine learning

• Model serving: REST APIs, Docker, Kubernetes

• Cloud platforms: AWS SageMaker, Google Cloud AI, Azure ML

• Hands-on: Deploying an ML model to production

Module 5.2: Edge AI & Optimization

• Edge AI deployment - 2025 trend

• Model compression and quantization

• TensorFlow Lite and ONNX

• Federated learning basics

• On-device ML applications

• Lab: Optimizing models for edge devices

Afternoon Session (2:00 PM - 5:30 PM)

Module 5.3: AI Ethics, Bias & Explainability

• Ethical AI principles - Critical 2025 focus

• Bias detection and mitigation

• Fairness in AI systems

• Explainable AI (XAI): LIME, SHAP

• Privacy-preserving ML techniques

• AI governance and compliance

• Workshop: Auditing models for bias

Module 5.4: Capstone Project & Future Trends

• Final project presentations

• Industry best practices and case studies

• 2025-2026 AI/ML trends:

  • Multimodal AI systems

  • Quantum machine learning

  • AI for sustainability

  • Neuromorphic computing

• Career pathways in AI/ML

• Continuous learning resources

• Course completion and certification

🎯 Course Learning Outcomes

By the end of this 5-day intensive training, participants will be able to:

1. ✅ Understand core AI and ML concepts with practical implementation skills

2. ✅ Build and train supervised and unsupervised learning models

3. ✅ Develop deep learning solutions using neural networks

4. ✅ Work with Generative AI and Large Language Models

5. ✅ Deploy ML models to production using MLOps best practices

6. ✅ Apply ethical AI principles and ensure model explainability

7. ✅ Stay current with 2025 AI/ML trends and emerging technologies

👥 Who Should Attend?

• Data scientists and analysts transitioning to AI/ML

• Software developers and engineers

• Business analysts and product managers

• IT professionals seeking AI skills

• Students and career changers

• Anyone interested in AI and Machine Learning careers

📚 Prerequisites

• Basic programming knowledge (Python preferred)

• Understanding of basic mathematics (algebra, statistics)

• Laptop with 8GB+ RAM

• Enthusiasm to learn cutting-edge AI technologies

🛠️ Tools & Technologies Covered

• Programming: Python, Jupyter Notebooks

• ML Libraries: Scikit-learn, NumPy, Pandas

• Deep Learning: TensorFlow, PyTorch, Keras

• Generative AI: OpenAI API, Hugging Face, Stable Diffusion

• MLOps: MLflow, Docker, Git

• Cloud Platforms: AWS, Google Cloud, Azure

• Visualization: Matplotlib, Seaborn, Plotly

🏆 Certification

Participants receive a Certificate of Completion in Artificial Intelligence & Machine Learning upon successfully completing all modules and the capstone project.

💡 Key Features

✨ Hands-on learning with real-world projects
✨ Industry-expert instructors with practical experience
✨ Latest 2025 AI trends integrated throughout
✨ Capstone project for portfolio building
✨ Post-course support and learning resources
✨ Small batch sizes for personalized attention

📞 Enrollment Information

Course Duration: 5 Days (Intensive)
Schedule: 9:00 AM - 5:30 PM daily
Format: Live Online

Early bird discounts and group enrollment offers available!