AI Training

AI Course Syllabus – Best Artificial Intelligence Training in Trichy
Looking to learn AI from scratch or upgrade your tech skills? Our Artificial Intelligence training in Trichy is divided into well-structured, industry-relevant modules covering everything from Python programming to Generative AI and real-world deployment.

Module 1: Introduction to Artificial Intelligence
• What is AI, ML, DL, and Data Science? – Differences explained
• History and Evolution of AI – From Alan Turing to ChatGPT
• Types of AI: Narrow AI, General AI, Super AI
• AI Applications in Real World Industries
• Setting Up the AI Development Environment (Python, Jupyter, Anaconda)
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Module 2: Python for AI & Data Science
• Python Basics: Variables, Data Types, Loops, Functions
• NumPy – Numerical computing and array operations
• Pandas – Data manipulation and analysis
• Matplotlib & Seaborn – Data visualization
• Working with Datasets and CSV/JSON files
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Module 3: Machine Learning (ML)
• Supervised Learning: Linear Regression, Logistic Regression, Decision Trees
• Unsupervised Learning: K-Means Clustering, PCA, DBSCAN
• Model Evaluation: Accuracy, Precision, Recall, F1-Score, ROC-AUC
• Feature Engineering and Data Preprocessing
• Scikit-Learn: Building and Training ML Models
• Random Forest, SVM, XGBoost & Ensemble Methods
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Module 4: Deep Learning & Neural Networks
• Artificial Neural Networks (ANN) – Architecture and training
• Activation Functions: ReLU, Sigmoid, Softmax, Tanh
• Backpropagation and Gradient Descent
• Convolutional Neural Networks (CNN) – For image data
• Recurrent Neural Networks (RNN) and LSTM – For sequential data
• TensorFlow and Keras – Building deep learning models
• PyTorch Fundamentals for Deep Learning
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Module 5: Natural Language Processing (NLP)
• Text Preprocessing: Tokenization, Stemming, Lemmatization
• Bag of Words, TF-IDF, and Word Embeddings (Word2Vec, GloVe)
• Sentiment Analysis and Text Classification
• Named Entity Recognition (NER) and POS Tagging
• Building Chatbots using NLP techniques
• Transformer Models: BERT, GPT – Introduction
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Module 6: Computer Vision
• Image Processing with OpenCV
• Object Detection: YOLO, SSD, Faster R-CNN
• Face Recognition and Facial Landmark Detection
• Image Segmentation: Semantic and Instance Segmentation
• Real-time Video Analysis and Motion Detection
• Deploying Computer Vision Models on Edge Devices
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Module 7: Generative AI & Large Language Models (LLMs)
• Introduction to Generative AI – GANs, VAEs, Diffusion Models
• Prompt Engineering – Writing effective prompts for AI tools
• Working with OpenAI GPT APIs and ChatGPT
• Fine-tuning LLMs with your own data (RAG, LoRA)
• Hugging Face Transformers – Using pre-trained models
• Building AI-powered Apps: Chatbots, AI Writers, Code Assistants
• Langchain Framework for LLM Applications
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Module 8: AI Projects, Tools & Deployment
• End-to-End AI Project Development from scratch
• Model Deployment using Flask and FastAPI
• Deploying AI on Cloud Platforms: AWS, Azure, Google Cloud
• Docker & Containerization for AI Apps
• MLOps Fundamentals: Model Monitoring & CI/CD for AI
• GitHub Portfolio: Push and showcase AI projects
• AI Interview Preparation and Resume Building
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