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Artificial Intelligence & Machine Learning Syllabus
Introduction to Artificial Intelligence & Machine Learning
Overview of AI and ML Concepts
Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
AI Applications in Real-World Scenarios
Python for AI & ML
Python Fundamentals
Data Structures, Functions, and Libraries (NumPy, Pandas)
Data Visualization with Matplotlib & Seaborn
Data Preprocessing & Analysis
Data Cleaning, Normalization, and Transformation
Feature Selection & Dimensionality Reduction
Exploratory Data Analysis (EDA)
Machine Learning Algorithms
Regression & Classification Models
Decision Trees, Random Forest, and SVM
Model Evaluation and Optimization Techniques
Deep Learning
ntroduction to Neural Networks
TensorFlow & Keras Frameworks
CNNs, RNNs, and LSTMs for Image and Text Processing
Natural Language Processing (NLP)
Text Preprocessing and Tokenization
Sentiment Analysis & Chatbot Development
Transformer Models and Generative AI
Computer Vision
Image Classification and Object Detection
Face Recognition Techniques
Real-Time Vision Applications
AI Tools & Frameworks
Scikit-learn, TensorFlow, PyTorch
ML Model Deployment with Flask or Streamlit
Cloud AI Platforms (AWS, Google AI, Azure ML)
Capstone Project & Internship
- End-to-End AI/ML Project Implementation
- Real-Time Case Studies and Industry Scenarios
- Internship / Portfolio Development
Testimonials
Student Stories
Rahul S
Priya M
Arjun K
Sneha R
Frequently Asked Questions
