Course Overview

Our Data Science course is designed with the jobs available in the IT sector in mind, and we require our learners to work with real data. These courses provide you with a thorough understanding of the IT industry and teach you how to think like a developer. 

This course also includes Machine learning and AI that equip students with the skills necessary to complete activities that go above and beyond those listed in job descriptions, which increases their chances of employability. Depending on the course type they have taken, once our learners have successfully completed one of these courses, they can work on any future tools that may be released that are similar.

This data science and machine learning certification gives learners a thorough understanding of key subjects like Mathematics & Statistics, SQL, Python, and Machine Learning. Mathematical ideas that are crucial to data analysis will be taught to students, including probability, statistics, and linear algebra. Additionally, they will learn SQL, a programming language that is essential for managing and modifying databases and for the retrieval and storage of data.

The versatile programming language Python, which is widely used in data science training, will also be taught to the learners. The last thing that you will learn is Machine Learning, a subfield of Artificial Intelligence (AI) that allows computers to learn from data and get better over time, which is essential in predictive modelling and pattern recognition. The overall goal of this course is to give students the information and abilities needed to thrive in the constantly expanding profession of data science.

Suitable for

This course is suitable for

  • Individuals who want to learn more about machine learning and data analysis
  • Beginners who want to build a solid foundation in fundamental subjects like Python Programming and Machine Learning.
  • Those who have some past experience in the field and desire to improve the quality of their skills and understanding.
  • Students who wish to create analytical models and make data-driven recommendations.

Course Modules

Core Modules

  • Mathematics
    • Linear Functions
    • Linear Algebra
    • Vectors
    • Matrices
    • Tensors
  • Statistics
    • Descriptive
    • Variability
    • Distribution
    • Probability
  • 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
  • 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 (Date Time)
  • File Management
  • Multiple Activities
  • 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
  • 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
Add-on Modules

  • Create an outstanding-looking developer portfolio on GitHub to demonstrate your code or share it with the world after learning the basics of version control using git.
  • Learn how to use one of the most popular libraries, TkInter, to construct unique custom graphical user interface apps. Additionally, you'll learn how to effectively use Python's built-in SQLite database.


Machine Learning Projects
  • Weather Prediction (Time Series Data)
  • Predicting cancer malignant or benign based on Data
  • Predicting Stock Prices
  • Hand Written Digit Recognition
  • Wine Quality Prediction
  • Marketing Data Analysis
  • Taxi Fare Prediction
Master Project in Data Science, Machine Learning & AI Training
  • A.I. plays Super Mario game: A Reinforcement Learning project to demonstrate your practical knowledge.
  • A.I. Sales Agent: A project demonstrating your practical skills to the recruiter using Reinforcement Learning, Deep Learning, and Natural Language Processing (NLP).
  • Text-to-Image & Image-to-Text Generation Tool: 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).

Skills you will gain after completing Data Science Training

  • Use of Mathematical Principles
  • SQL mastery
  • Data analysis and visualisation skills
  • Data cleaning and preprocessing methods
  • Learn Python and become an expert in machine learning
  • Machine Learning Models
  • Algorithms for artificial intelligence
  • Data management, Data ethics, and Analysis

Jobs you can Apply after Data Science Course

  • Database Administrator
  • Data Scientist
  • Python Developer
  • Data Analyst
  • Machine Learning Engineer


  • Certification in Data Science, Machine Learning, and AI

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