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AI & Data Science Bootcamp

Tech And Innovation

AI & Data Science Bootcamp

info@earnify-edu.com info@earnify-edu.com

 

This intensive bootcamp is designed to take you from beginner to job-ready AI & Data Science professional, even if you have no prior coding background.

You will learn how to analyze real-world data, build machine learning models, and deploy AI-powered applications using industry-standard tools like Python, NumPy, Pandas, Scikit-Learn, TensorFlow, and Streamlit.

 

Course Synopsis :

The AI & Data Science Bootcamp is a live, hands-on program that teaches how to analyze data and build intelligent predictive models. Students learn the complete workflow from data preparation and visualization to machine learning and evaluation. By the end of the course, learners can apply AI techniques to solve real-world problems using practical datasets.

 

Course Learning Objectives:

By the end of this program, learners will be able to:

  1. Understand the complete data science workflow from data collection to model evaluation.

  2. Clean, analyze, and visualize datasets to extract meaningful insights.

  3. Apply machine learning algorithms for prediction and classification tasks.

  4. Interpret model performance and improve results through optimization.

  5. Solve real-world problems using practical data-driven approaches.

 

 

 

Module Details

 

Module 1 - Foundations of AI & Data Science

Understanding how data becomes intelligence

What you will understand
This module introduces how data science and AI work together to solve problems. You will learn the overall workflow from raw data to intelligent decision-making.

What you will do

  • Understand data science pipeline

  • Identify problem types (prediction vs classification)

  • Set up working environment

Tools used
Python, Jupyter Notebook, Anaconda / VS Code

Outcome
After this module you can understand where AI fits in real-world problem solving.

 

Module 2 - Python for Data Analysis

Using programming to work with data

What you will understand
You will learn how Python is used to manipulate and process datasets efficiently.

What you will do

  • Write Python scripts

  • Work with variables, loops and functions

  • Handle data structures

Tools used
Python, NumPy basics

Outcome
After this module you can programmatically work with datasets.

 

Module 3 - Data Handling & Cleaning

Preparing messy data for analysis

What you will understand
Real-world data is incomplete and inconsistent. This module teaches how to prepare usable datasets.

What you will do

  • Load datasets

  • Handle missing values

  • Filter and transform data

Tools used
Pandas

Outcome
After this module you can convert raw data into clean analysis-ready data.

 

Module 4 - Data Visualization & Exploration

Finding patterns inside data

What you will understand
Learn how visual analysis reveals trends and relationships in data.

What you will do

  • Create plots and charts

  • Compare variables

  • Interpret patterns

Tools used
Matplotlib, Seaborn

Outcome
After this module you can extract insights from datasets visually.

 

Module 5 - Machine Learning Models

Teaching machines to make predictions

What you will understand
This module explains how machines learn from examples to predict future outcomes.

What you will do

  • Train classification models

  • Build regression models

  • Evaluate performance

Tools used
Scikit-learn

Outcome
After this module you can build predictive machine learning models.

 

Module 6 - Model Improvement & Evaluation

Making predictions reliable

What you will understand
Learn how to measure accuracy and improve model performance.

What you will do

  • Tune parameters

  • Compare models

  • Reduce errors

Tools used
Scikit-learn metrics

Outcome
After this module you can optimize models for better results.

 

Module 7 - Introduction to Deep Learning

Working with complex patterns

What you will understand
Understand how neural networks handle complex data like images and large datasets.

What you will do

  • Build basic neural networks

  • Train deep learning models

Tools used
TensorFlow / Keras

Outcome
After this module you understand how modern AI systems learn complex relationships.

 

Module 8 - Final Project & Deployment

Applying everything in a real problem

What you will understand
Combine all concepts into a complete data science solution.

What you will do

  • Work on real dataset

  • Build end-to-end project

  • Present results

Tools used
Full stack from previous modules

Outcome
After this module you will have a complete AI project for your portfolio.

 

 

Software & Technologies Used

Category Tools / Notes
Programming Python
Development Environment Jupyter Notebook, VS Code
Data Handling Pandas, NumPy
Data Visualization Matplotlib, Seaborn
Machine Learning Scikit-learn
Deep Learning TensorFlow / Keras
Model Evaluation Scikit-learn Metrics
Version Control GitHub
Data Sources Real-world datasets (CSV, structured data)
Docs & Delivery Earnify LMS + Zoom live sessions, recordings

 

English

100+ Lectures

25h
251 Students
Last Updated: February 19, 2026

What you'll learn

This course includes

    • Develop the ability to analyze real-world datasets and extract meaningful insights.

    • Build and evaluate machine learning models for prediction and decision-making tasks.

    Apply data-driven reasoning to solve practical business and technical problems.

    Design complete end-to-end AI workflows from data preparation to model deployment.

    Create a portfolio-ready project demonstrating practical data science competency.

    Overview
    Curriculum
    • 8 Sections
    • 7 Quizzes
    • 23 Zooms
    • 16 Assignments
    • 25h Duration
    Collapse All
    Module 1: Introduction to Data Science & ML
    1 Quiz3 Zooms2 Assignments
    1. Intro to Machine Learning & Data Science
    2. Write a short summary of ML & DS goals
    3. Python for Data Handling (Lists & Data Types)
    4. Python basics & environment setup
    5. Python Environment Setup & Tools
    6. Python practice file submission
    Module 2: Python for Data Science
    1 Quiz3 Zooms2 Assignments
    1. Pandas – DataFrames & CSV Handling
    2. Pandas basics
    3. NumPy – Arrays & Operations
    4. NumPy array practice
    5. Data Exploration & Cleaning
    6. Clean a sample dataset (upload)
    Module 3: Data Handling & Cleaning
    1 Quiz3 Zooms2 Assignments
    1. Data Visualization (Matplotlib)
    2. Create 3 plots from dataset
    3. Data Visualization (Seaborn)
    4. Visualization interpretation
    5. Train/Test Split & Evaluation Basics
    6. Train/test dataset split task
    Module 4: Data Visualization
    1 Quiz3 Zooms2 Assignments
    1. Scikit-Learn – Intro & Supervised Learning
    2. Supervised learning concepts
    3. Classification (Logistic Regression)
    4. Build & evaluate classifier
    5. Regression (Linear & Polynomial)
    6. Regression model results
    Module 5: Machine Learning with Scikit-Learn
    1 Quiz3 Zooms2 Assignments
    1. Decision Trees & Random Forests
    2. Tree model basics
    3. Ensemble Learning & Hyperparameter Tuning
    4. Tune model hyperparameters
    5. Model Evaluation & Metrics
    6. Confusion matrix & metrics report
    Module 6: Advanced ML Models
    1 Quiz3 Zooms2 Assignments
    1. Introduction to Neural Networks
    2. NN basic terms
    3. TensorFlow & Deep Learning Setup
    4. Build & train basic CNN
    5. CNN for Image Classification
    6. First TensorFlow model script
    Module 7: Deep Learning
    1 Quiz3 Zooms2 Assignments
    1. Transfer Learning Techniques
    2. Transfer learning benefits
    3. Data Science Projects & Workflow
    4. Draft project plan
    5. Real-World ML Case Study
    6. Case study report
    Module 8: Projects & Deployment
    2 Zooms2 Assignments
    1. Final Project Development
    2. Start final project
    3. Final Project Presentations
    4. Submit final project

     

    This intensive bootcamp is designed to take you from beginner to job-ready AI & Data Science professional, even if you have no prior coding background.

    You will learn how to analyze real-world data, build machine learning models, and deploy AI-powered applications using industry-standard tools like Python, NumPy, Pandas, Scikit-Learn, TensorFlow, and Streamlit.

     

    Course Synopsis :

    The AI & Data Science Bootcamp is a live, hands-on program that teaches how to analyze data and build intelligent predictive models. Students learn the complete workflow from data preparation and visualization to machine learning and evaluation. By the end of the course, learners can apply AI techniques to solve real-world problems using practical datasets.

     

    Course Learning Objectives:

    By the end of this program, learners will be able to:

    1. Understand the complete data science workflow from data collection to model evaluation.

    2. Clean, analyze, and visualize datasets to extract meaningful insights.

    3. Apply machine learning algorithms for prediction and classification tasks.

    4. Interpret model performance and improve results through optimization.

    5. Solve real-world problems using practical data-driven approaches.

     

     

     

    Module Details

     

    Module 1 - Foundations of AI & Data Science

    Understanding how data becomes intelligence

    What you will understand
    This module introduces how data science and AI work together to solve problems. You will learn the overall workflow from raw data to intelligent decision-making.

    What you will do

    • Understand data science pipeline

    • Identify problem types (prediction vs classification)

    • Set up working environment

    Tools used
    Python, Jupyter Notebook, Anaconda / VS Code

    Outcome
    After this module you can understand where AI fits in real-world problem solving.

     

    Module 2 - Python for Data Analysis

    Using programming to work with data

    What you will understand
    You will learn how Python is used to manipulate and process datasets efficiently.

    What you will do

    • Write Python scripts

    • Work with variables, loops and functions

    • Handle data structures

    Tools used
    Python, NumPy basics

    Outcome
    After this module you can programmatically work with datasets.

     

    Module 3 - Data Handling & Cleaning

    Preparing messy data for analysis

    What you will understand
    Real-world data is incomplete and inconsistent. This module teaches how to prepare usable datasets.

    What you will do

    • Load datasets

    • Handle missing values

    • Filter and transform data

    Tools used
    Pandas

    Outcome
    After this module you can convert raw data into clean analysis-ready data.

     

    Module 4 - Data Visualization & Exploration

    Finding patterns inside data

    What you will understand
    Learn how visual analysis reveals trends and relationships in data.

    What you will do

    • Create plots and charts

    • Compare variables

    • Interpret patterns

    Tools used
    Matplotlib, Seaborn

    Outcome
    After this module you can extract insights from datasets visually.

     

    Module 5 - Machine Learning Models

    Teaching machines to make predictions

    What you will understand
    This module explains how machines learn from examples to predict future outcomes.

    What you will do

    • Train classification models

    • Build regression models

    • Evaluate performance

    Tools used
    Scikit-learn

    Outcome
    After this module you can build predictive machine learning models.

     

    Module 6 - Model Improvement & Evaluation

    Making predictions reliable

    What you will understand
    Learn how to measure accuracy and improve model performance.

    What you will do

    • Tune parameters

    • Compare models

    • Reduce errors

    Tools used
    Scikit-learn metrics

    Outcome
    After this module you can optimize models for better results.

     

    Module 7 - Introduction to Deep Learning

    Working with complex patterns

    What you will understand
    Understand how neural networks handle complex data like images and large datasets.

    What you will do

    • Build basic neural networks

    • Train deep learning models

    Tools used
    TensorFlow / Keras

    Outcome
    After this module you understand how modern AI systems learn complex relationships.

     

    Module 8 - Final Project & Deployment

    Applying everything in a real problem

    What you will understand
    Combine all concepts into a complete data science solution.

    What you will do

    • Work on real dataset

    • Build end-to-end project

    • Present results

    Tools used
    Full stack from previous modules

    Outcome
    After this module you will have a complete AI project for your portfolio.

     

     

    Software & Technologies Used

    Category Tools / Notes
    Programming Python
    Development Environment Jupyter Notebook, VS Code
    Data Handling Pandas, NumPy
    Data Visualization Matplotlib, Seaborn
    Machine Learning Scikit-learn
    Deep Learning TensorFlow / Keras
    Model Evaluation Scikit-learn Metrics
    Version Control GitHub
    Data Sources Real-world datasets (CSV, structured data)
    Docs & Delivery Earnify LMS + Zoom live sessions, recordings

     

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