Data Science, ML & AI Course

Course Syllabus

• Math for Data Science
  • Algebra fundamentals
  • Linear algebra (Vectors, matrices, Dot product)
  • Descriptive Statistics
  • Probability fundamentals
  • Distributions
  • Graph interpretation
• Python
  • Python programing
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
• SQL (MySQL)
• Exploratory Data Analysis (EDA)
  • Data Cleaning & Preparation
  • Data Manipulation & Transformation
  • Exploratory Data Analysis
  • Data Visualization
• Supervised Learning
  • Linear Regression
  • Polynomial Regression
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Support Vector Machines (SVM)
  • L1 and L2 regularization
  • Decision Trees
  • Random Forest
  • Gradient Boosting
  • XGBoost
• Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction (PCA)
• Model Understanding & Evaluation
  • Model training and evaluation
  • Overfitting, underfitting, and bias–variance
  • Choosing the right model for the problem
• Neural Network Models
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • LSTM networks
• Classical NLP & Speech
  • Text processing basics
  • Tokenization and stemming
  • Traditional NLP pipelines
  • Sentiment analysis
  • Speech recognition
• Computer Vision
  • Introduction to OpenCV
  • Image processing fundamentals
  • Optical Character Recognition (OCR)
  • Object detection concepts
  • YOLO architecture
• Modern Generative AI
  • Transformers
  • Attention mechanism
  • Large Language Models (LLMs)
  • Prompt engineering and effective prompting
  • Fine-Tuning
  • Image generation using Diffusion models
  • Retrieval Augmented Generation (RAG)
  • Fine-tuning vs prompting
  • Agent-based AI systems
  • Understanding current AI tools and platforms
• Deployment & MLOps
  • Deployment
  • Version control and collaboration
  • APIs and model serving
  • Understanding MLOps concepts
  • Containers
  • Orchestration
  • Cloud basics
  • CI/CD concepts for ML systems