Data Science, Machine Learning, and AI Course

Mathematics and statistics are crucial in machine learning, providing tools for creating models that learn from data and make accurate predictions. Linear algebra, calculus, and probability theory are used in algorithms, while statistics analyzes data, identifies patterns, and predicts outcomes. Strong knowledge in these areas is necessary for success in machine learning.

1. Mathematics

• Linear Functions
• Linear Algebra
• Vectors
• Matrices
• Tensors

2. Statistics

• Descriptive
• Variability
• Distribution
• Probability

SQL is critical in machine learning, allowing efficient data retrieval and analysis from relational databases. It ensures accurate analysis of large data sets, with solid understanding essential for success.

• DDL and DML in MySQL and setup
• ERD Diagrams and Relational Mapping
• Data normalisation
• Basic Queries
• Database Manipulation
• Table Manipulation
• Relational Algebra
• Advanced SQL - Joining, Subquery, Views
• Database Security
• Multiple Activities to Perform

Python is a widely-used programming language in machine learning. It offers powerful libraries, tools, and frameworks for data analysis, modeling, and visualization. Python's simplicity and flexibility make it a popular choice for building machine learning models and deploying them in real-world applications.

• Python Setup and What is Python?
• Data Types and Syntax
• Comparison Operators
• Python Loop
• Python Statements
• Logical Operators
• Methods and Functions
• Error and Exception Handling
• Modules Packages and libraries
• Debugging
• Advanced python Modules (DateTime)
• File Management
• Multiple Activities to Perform
• Multiple Projects to Build

• What is Docker?
• Why Docker?
• How Docker helps us in ML/Software development?
• Docker Basics
• Difference between Container and Image
• Creating our own Docker Image
• Docker Registry
• Push Docker Images in Docker Registry
• Pull Docker Images from Docker Registry

Machine learning is a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It involves the use of algorithms, statistical models, and data to build predictive models and uncover insights from large datasets.

• Data Preprocessing

Supervised Learning

• Regression Models
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Random Forest Regression
  • Topics such as
    • Assessing a Regression Model
    • Bias vs Variance
    • Regularisation
    • Gradient Descent
• Classification Models
  • Decision Tree Classification
  • K-Nearest Neighbor
  • Logistic Regression
  • Na誰ve Bayes
  • Random Forest Classification
  • Support Vector Machines
  • Additional Topics
    • Assessing a Classification Model
    • Adaboost
    • Gradient Boosting
    • XGBoost
    • Grid Search CV

Unsupervised Learning

• Clustering Models
  • Hierarchical
  • K-Means Clustering
• Association
  • Apriori
  • Eclat
• Build Dashboards for Data Visualisation
• Solved Sample Code Files for easy practice
• Access to Multiple Datasets
• 7+ Real world data projects

• Reinforcement Learning
  • Thompson Sampling
  • Upper Confidence Bound (UCB)
  • Q-Learning
• Natural Language Processing (NLP)
• Deep Learning
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Encoder Decoder
  • Stable Diffusion Model
  • Transformer Model
  • Large Language Model Development (LLMs)
    • Langchain
    • Chatgpt
    • Hugging face Api
    • Text Embedding’s
    • Vector Databases
    • Retrieval Augmented Generation (RAG Bot)
  • Additional Topic
    • LSTM
• Solved Sample Code Files for easy practice
• Access to Multiple Datasets
• 4 Master A.I. Projects

This module provides a comprehensive overview of Jenkins, a leading automation server widely used for continuous integration and continuous delivery (CI/CD) processes. Participants will delve into the fundamentals of Jenkins, its operations, and integration with other tools such as GitHub for creating efficient CI/CD pipelines.

• Jenkins Intro
• Jenkins Basics
• Jenkins Operations
• GitHub Integration & CI/CD Test Pipeline

Learn the fundamentals of version control via git and create your amazing looking developer portfolio on github to showcase your code or share your code with the world.

Learn to create custom custom graphical user interface applications using one of the widely used library, TkInter. You will also learn to use the inbuild database within python, SQLite.

A project on Reinforcement Learning to display your practical skills to the RECRUITER.

A project on Reinforcement Learning, Deep Learning and Natural Language Processing (NLP) Learning to display your practical skills to the RECRUITER.

A project on Large Language Model (LLM) to generate image from a prompt given (Text-to-Image) and to generate the description of the image from a given image (Image-to-Text).