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Artificial Intelligence & Machine Learning Course Syllabus

Course Overview

This comprehensive Artificial Intelligence & Machine Learning course is designed to equip learners with practical skills in AI/ML technologies that are transforming industries worldwide. The course covers the complete AI/ML stack, from foundational concepts to advanced applications in Deep Learning, Natural Language Processing, Computer Vision, and Generative AI. Throughout 60 hours of intensive training, you will master Python for data science, implement machine learning algorithms, build neural networks, process natural language, perform computer vision tasks, and work with cutting-edge Large Language Models. This course combines theoretical understanding with hands-on projects, enabling you to build production-ready AI solutions and advance your career in the rapidly growing AI field.

Course Summary

Course Name Artificial Intelligence & Machine Learning
Duration 60 Hours
Mode of Training Online / Classroom
Training Fee Contact for Pricing
Certificate Yes (Industry-Recognized Certificate upon completion)
Prerequisites Basic Python knowledge, Mathematics (Linear Algebra, Statistics, Calculus)

Detailed Course Syllabus

Module Topics Description % of Course
1 Introduction to AI & Machine Learning
  • History and Evolution of Artificial Intelligence
  • Types of AI (Narrow vs General AI, Weak vs Strong AI)
  • AI vs Machine Learning vs Deep Learning - Key Differences
  • Real-world Applications and Use Cases
  • AI Industry Trends and Future Scope
 10%
2 Python for AI & Data Science
  • NumPy: Array operations, linear algebra, mathematical functions
  • Pandas: DataFrames, data cleaning, manipulation, aggregation
  • Matplotlib & Seaborn: Data visualization techniques
  • Data Preprocessing: Handling missing data, outliers, scaling
  • EDA (Exploratory Data Analysis) techniques
 15%
3 Machine Learning Fundamentals
  • Supervised Learning: Regression and Classification
  • Unsupervised Learning: Clustering and Dimensionality Reduction
  • Reinforcement Learning: Basic Concepts and Applications
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM) and Ensemble Methods
  • Model Evaluation, Hyperparameter Tuning, Cross-validation
 20%
4 Deep Learning & Neural Networks
  • Artificial Neural Networks (ANN): Architecture and Training
  • Convolutional Neural Networks (CNN) for Image Processing
  • Recurrent Neural Networks (RNN) and LSTM Networks
  • Transformer Architecture and Attention Mechanisms
  • PyTorch and TensorFlow/Keras Frameworks
  • Training, Validation, and Optimization Techniques
 20%
5 Natural Language Processing (NLP)
  • Text Tokenization and Preprocessing
  • Sentiment Analysis: Polarity and Opinion Mining
  • Text Classification and Spam Detection
  • Word Embeddings (Word2Vec, GloVe, FastText)
  • BERT, GPT Models, and Large Language Models (LLMs)
  • Prompt Engineering and Fine-tuning LLMs
  • Named Entity Recognition (NER) and POS Tagging
 15%
6 Computer Vision
  • Image Classification with Deep Learning
  • Object Detection (YOLO, Faster R-CNN, RetinaNet)
  • Image Segmentation (Semantic and Instance)
  • OpenCV Library for Image Processing
  • Face Detection and Recognition
  • Transfer Learning in Computer Vision
 10%
7 Generative AI & LLMs
  • Introduction to Generative AI and Generative Models
  • ChatGPT API Integration and Usage
  • Azure OpenAI Services and Deployment
  • LangChain: Building LLM-powered Applications
  • Retrieval Augmented Generation (RAG)
  • Fine-tuning Models for Specific Domains
  • Building Chatbots and AI-powered Applications
 10%

Key Learning Outcomes

  • Foundational Knowledge: Understand AI/ML concepts, types, and real-world applications
  • Data Science Skills: Master Python libraries (NumPy, Pandas) for data manipulation and analysis
  • Machine Learning Implementation: Build and deploy supervised, unsupervised, and reinforcement learning models
  • Deep Learning Expertise: Design and train neural networks using PyTorch and TensorFlow
  • NLP Mastery: Process and analyze text data, work with state-of-the-art language models like BERT and GPT
  • Computer Vision Skills: Develop image classification, object detection, and segmentation systems
  • Generative AI Applications: Leverage LLMs, ChatGPT APIs, and LangChain to build intelligent applications
  • Project Experience: Complete real-world projects demonstrating end-to-end AI/ML solutions

Training Methodology

Our training approach combines theory with hands-on practice to ensure maximum learning outcomes:

  • Live Interactive Sessions: Learn directly from industry experts with real-time Q&A
  • Practical Projects: Work on capstone projects covering end-to-end AI/ML pipelines
  • Code-along Exercises: Practice coding alongside instructors using Jupyter notebooks
  • Real-world Datasets: Train models on actual industry datasets from Kaggle and other sources
  • Industry Best Practices: Learn deployment, optimization, and production considerations
  • Recorded Sessions: Access lifetime recordings of all classes for revision
  • Dedicated Support: Get help from mentors and the learning community

Who Should Enroll?

  • Software developers looking to transition into AI/ML roles
  • Data analysts seeking to advance to Data Science
  • Students aspiring for careers in Artificial Intelligence and Machine Learning
  • Professionals wanting to upskill in emerging AI technologies
  • Data scientists looking to deepen knowledge in Deep Learning and NLP
  • Anyone interested in building intelligent systems and applications

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