AI & Data Science Bootcamp
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:
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Understand the complete data science workflow from data collection to model evaluation.
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Clean, analyze, and visualize datasets to extract meaningful insights.
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Apply machine learning algorithms for prediction and classification tasks.
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Interpret model performance and improve results through optimization.
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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
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Understand data science pipeline
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Identify problem types (prediction vs classification)
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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
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Write Python scripts
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Work with variables, loops and functions
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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
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Load datasets
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Handle missing values
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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
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Create plots and charts
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Compare variables
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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
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Train classification models
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Build regression models
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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
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Tune parameters
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Compare models
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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
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Build basic neural networks
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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
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Work on real dataset
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Build end-to-end project
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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 |
