Artificial Intelligence Training Syllabus

Artificial Intelligence Training Syllabus:

This course introduces participants to Artificial Intelligence (AI) technologies, focusing on AI fundamentals, neural networks, and real-world AI applications. Through hands-on exercises and projects, students will learn how to apply AI concepts to solve real problems.

AI Foundations and Setting up Your Environment

📄 Lesson 1. Introduction to Artificial Intelligence: What is AI, history, key concepts, and industry applications

📄 Lesson 2. AI Programming Basics: Setting up Python for AI, understanding AI-specific libraries (e.g., TensorFlow, Keras, Scikit-learn)

📄 Lesson 3. Search Algorithms: Implementation of informed and uninformed search algorithms (e.g., BFS, DFS, A*)

📄 Lesson 4. Game Playing: AI for game strategies and decision-making (e.g., minimax algorithm, alpha-beta pruning)

📄 Lesson 5. Natural Language Processing (NLP): Basics of NLP, text processing, tokenization, and language models

📄 Lesson 6. Hands-on Project: Building a Simple Chatbot

📄 Lesson 7. Knowledge Representation and Reasoning: Introduction to knowledge graphs, ontology, and reasoning techniques

📄 Lesson 8. AI and Ethics: Discussion on ethical considerations in AI, bias in models, and AI regulation

📄 Lesson 9. Reinforcement Learning: Understanding reinforcement learning techniques and Markov decision processes

📄 Lesson 10. Hands-on Project: Training an AI Agent for Game Environment

📄 Lesson 11. AI for Real-World Applications: Case studies in AI-driven sectors like healthcare, finance, and autonomous systems

📄 Lesson 12. Final AI Project: Deploying an AI model in a real-world scenario

Course Duration

This course can be completed in 12 hours (2 days) or 36 hours (5-6 days), depending on the pace, with both theoretical and practical sessions.

Requirements

Basic understanding of Python
A computer with internet access

Course Outcomes

Upon completion, participants will:

Understand AI fundamentals
Apply AI techniques to solve practical problems
Build and deploy AI models

Previous Workshops

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Machine Learning Training Syllabus:

This course provides a comprehensive overview of Machine Learning concepts, techniques, and applications. Students will learn how to implement ML algorithms using Python and its libraries, including practical exercises and projects to solidify their understanding and skills in building and deploying machine learning models.

Introduction to Machine Learning

📄 Lesson 1. What is Machine Learning? Introduction, Applications, and Types

📄 Lesson 2. Setting Up the Environment: Installing Python and Libraries (NumPy, pandas, scikit-learn)

📄 Lesson 3. Data Preprocessing: Data Cleaning, Handling Missing Values, and Data Transformation

📄 Lesson 4. Exploratory Data Analysis: Visualization Techniques using Matplotlib and Seaborn

📄 Lesson 5. Introduction to Supervised Learning: Regression and Classification

📄 Lesson 6. Hands-on Project: Predicting House Prices using Linear Regression

📄 Lesson 7. Model Evaluation: Train/Test Split, Cross-Validation, Metrics (Accuracy, Precision, Recall)

📄 Lesson 8. Introduction to Unsupervised Learning: Clustering Techniques (K-Means, Hierarchical)

📄 Lesson 9. Dimensionality Reduction Techniques: PCA and t-SNE

📄 Lesson 10. Hands-on Project: Customer Segmentation using K-Means Clustering

📄 Lesson 11. Introduction to Neural Networks: Understanding the Basics

📄 Lesson 12. Deep Learning Fundamentals: Introduction to TensorFlow and Keras

📄 Lesson 13. Hands-on Project: Building a Simple Neural Network for Image Classification