AI & ML

The AI & ML course at VyTCDC is designed to equip learners with a comprehensive understanding of artificial intelligence and machine learning fundamentals. Participants will gain hands-on experience in building and deploying models using real-world datasets.

The course aims to develop skills in data analysis, model evaluation, and algorithm selection, empowering students to apply AI and ML solutions to diverse industries.

By the end of the program, students will be proficient in using popular tools and frameworks, enabling them to contribute effectively to innovative projects and advance their careers in the rapidly evolving tech landscape.

Artificial Intelligence (AI) and Machine Learning (ML) Course Syllabus:

• Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
• Understanding AI & ML
• Applications of AI & ML
• Setting up Python environment
• Python Programming Fundamentals
• Python Programming Fundamentals
• Variables, Datatypes & Operators
• Basic Input & Output Operations
• Control Flow Statement
• Functions & Modules
• Data Acquisition & Pre-processing
• Data Collection from different sources, Signal Processing
• Web API, Open Data Sources, Data API, Web Scrapping
• Handling numerical values with NumPy module
• Data Cleaning & Data Manipulation with Pandas module
• Data Transformation & Visualization
• MinMax, Standard scaler, z-score transformation
• Standardization and Normalization
• Sklearn. preprocessing module for transformation
• Data Visualization with Matplotlib module
• Supervised Learning Algorithms
• Regression - Linear Regression, Logistic Regression
• Classification: k-Nearest Neighbors (k-NN), Decision Trees and Random Forests
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score Unsupervised Learning
• Clustering Techniques: K-Means, Hierarchical Clustering
• Dimensionality Reduction: Principal Component Analysis (PCA)
• Anomaly Detection
• Recommender Systems
• Deep Learning
• Introduction to Deep Learning & Neural networks
• Activation Functions, Backpropagation
• Building a network with Tensor Flow & Keras
• Convolutional Neural Networks (CNNs)
• Large Language Models (LLMs)
• Introduction to LLMs
• Working & applications of LLMs
• Encoder-decoder models
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks
• Introduction to Natural Language Processing (NLP)
• Introduction to NLP and NLTK
• Text Pre-processing, Tokenization
• Sequence tagging, sentence structure
• Text Classification, Machine Translation
• Sentiment Analysis
• Ethics and Future Trends
• Bias and Fairness in AI
• Privacy Concerns
• Explainable AI
• Future Trends in AI and ML