AI Integrated Data Science & Machine Learning Training Program
Build practical data science and machine learning skills that help you move from raw files and business questions to trained models, deployed APIs and portfolio-ready projects. Learn the tools, workflow and professional habits used in modern data teams.
Practical coding, notes, video references, project guidance and revision resources are included throughout the training journey.
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Data science is not only about models. It is about solving useful problems with data.
Real data science work starts with messy files, unclear questions and imperfect information. Strong practitioners know how to clean data, understand the business context, choose the right model, evaluate it honestly and deliver the result in a usable form.
Work with imperfect data.
Handle files, databases, missing values, duplicates, formats and real-world data-quality problems.
Find meaningful patterns.
Use statistics, SQL, visualisation and EDA to understand what the data is actually saying.
Build and evaluate models.
Train models for regression, classification, clustering and time-based forecasting with the right workflow.
Make the work usable.
Expose models through APIs, package them with Docker and present projects professionally through GitHub.
Go from raw data to a deployed ML project.
This program is designed as a connected journey, so you understand why each skill matters before moving to the next one.
- Start with Python, files, notebooks, Git and clean coding habits
- Build data-handling confidence with NumPy, Pandas, SQL and EDA
- Learn statistics and mathematics before relying on machine learning libraries
- Train, evaluate, deploy and document complete models with a repeatable workflow
Learn why the model works
Understand statistics, probability, linear algebra and gradients so model results are not treated like a black box.
Build reliable workflows
Use validation, preprocessing, pipelines and cross-validation to reduce leakage and make experiments more repeatable.
Work with real project constraints
Handle missing files, invalid values, imbalanced classes, imperfect datasets and deployment considerations.
Present work professionally
Build GitHub repositories, project documentation, model APIs and portfolio stories that make your work easier to evaluate.
Use AI to improve research, reasoning and iteration, not to replace scientific thinking.
AI tools can speed up code explanation, experiment planning, debugging, documentation and technical research. The strongest data professionals still validate results against the data, test assumptions and own every final decision.
Explain Python and Scikit-learn code, troubleshoot errors, outline feature-engineering steps and draft experiment notes.
Review model logic, clarify trade-offs, examine evaluation choices and improve technical explanations for stakeholders.
Explore model ideas, data-preparation approaches, deployment choices and practical ways to break down a new ML task.
Research datasets, official documentation, library updates, MLOps concepts and domain context with source-based discovery.
Machine learning skill is the foundation. AI-ready workflows can make experimentation more focused.
The advantage is not blindly accepting generated code. It is moving from a vague problem to a tested, explainable and deployable solution with clearer reasoning and faster iteration.
Can build models, but may spend more time on repeated research, debugging and documentation tasks.
Technical ability is essential. Yet unfamiliar datasets, model-selection questions and deployment issues can slow progress when every investigation begins from scratch.
- Researches errors, metrics and library options one at a time
- May need more time to structure an experiment plan
- Writes documentation and project explanations manually
- Can find it slower to compare alternative approaches
Can combine technical foundations with faster research, clearer experiments and better review habits.
AI becomes useful only when you can ask specific questions, test output, detect problems and make the final analytical judgement yourself.
- Breaks complex ML problems into clear data, model and evaluation stages
- Explores documentation, model options and feature ideas more efficiently
- Uses AI to support debugging, test cases and project documentation
- Creates a first solution faster, then validates and improves it rigorously
Learn the full cycle, not only the algorithm names.
Every project follows a consistent workflow that helps you move from a business question to a model that can be evaluated and shared.
Frame the Problem
Define the objective, success measure, constraints and type of ML task.
Prepare Data
Load files, clean errors, explore quality and create usable features.
Build a Baseline
Start simple, establish a benchmark and avoid unnecessary complexity.
Train & Tune
Use preprocessing, pipelines, cross-validation and model comparison carefully.
Evaluate Honestly
Check metrics, class balance, leakage risks and real-world usefulness.
Deploy & Document
Serve predictions through an API, package the project and explain the work clearly.
One ordered curriculum from Python to MLOps.
Each four-week block ends with a practical outcome, so learners develop an expanding project portfolio instead of only completing isolated exercises.
Weeks 1–4 · Python, Files & Development Setup
Concepts to learn
- Python revision, functions, modules and object-oriented concepts
- Virtual environments, Jupyter Notebook, VS Code and Git/GitHub
- Exception handling, logging and defensive programming
- File handling with CSV, JSON and Excel files
Weeks 5–8 · NumPy & Pandas Foundations
Concepts to learn
- NumPy arrays, indexing, slicing, reshaping, vectorisation and broadcasting
- Pandas Series and DataFrames
- Filtering, sorting, missing values, duplicates and data types
Weeks 9–12 · Data Wrangling & Excel Reporting
Concepts to learn
- Pandas groupby, aggregation, merge, join and concat
- Pivot tables, crosstab and datetime handling
- Text cleaning, regular expressions and Excel reporting
Weeks 13–16 · SQL & Relational Data
Concepts to learn
- SQL basics, database design, tables, primary keys and foreign keys
- CRUD operations, SELECT, WHERE, ORDER BY, GROUP BY and HAVING
- Joins for combining customer, order, product and payment data
Weeks 17–20 · Advanced SQL, APIs & Python-MySQL
Concepts to learn
- SQL subqueries, CTEs, window functions and indexes
- Python-MySQL connection and data extraction
- APIs and JSON response handling
Weeks 21–24 · Visualisation, EDA & Storytelling
Concepts to learn
- Matplotlib and Seaborn
- Chart selection, histograms, box plots, scatter plots, heatmaps, bar charts and line charts
- Dashboard storytelling and business insight communication
Weeks 25–28 · Statistics for Data Science
Concepts to learn
- Mean, median, mode, variance, standard deviation and quartiles
- Skewness, outliers, correlation and covariance
- Interpreting normal, unusual and misleading values
Weeks 29–32 · Probability, Testing & A/B Analysis
Concepts to learn
- Probability, conditional probability and Bayes theorem
- Random variables, distributions, sampling and Central Limit Theorem
- Confidence intervals, hypothesis testing and A/B testing
Weeks 33–36 · Mathematics for Machine Learning
Concepts to learn
- Vectors, matrices, dot product, matrix multiplication and transpose
- Eigenvalues and eigenvectors
- Derivatives, gradients and gradient descent
Weeks 37–40 · Machine Learning Workflow
Concepts to learn
- Supervised vs unsupervised learning and train-validation-test split
- Baseline models, data leakage, preprocessing, encoding and scaling
- Pipelines and cross-validation
Weeks 41–44 · Regression Models
Concepts to learn
- Linear and polynomial regression
- Ridge, Lasso and Elastic Net
- Decision tree, random forest and gradient boosting regression
Weeks 45–48 · Classification Models
Concepts to learn
- Logistic regression, KNN, Naive Bayes and SVM
- Decision trees, random forest and gradient boosting
- Confusion matrix, precision, recall and imbalanced data
Weeks 49–52 · Advanced ML, Deep Learning & MLOps
Concepts to learn
- Feature engineering, transformations, feature selection and PCA
- Clustering, anomaly detection and time-series basics
- Deep learning foundations and model-selection awareness
- FastAPI, Docker, deployment, GitHub documentation and MLOps fundamentals
One year to build the full data science and ML foundation.
This is a long-form path for learners who want time to build fundamentals, complete projects, develop deployment exposure and create stronger portfolio proof.
12-Month Training Program
Move from Python, files and SQL to statistics, machine learning, deep learning foundations, APIs, deployment and MLOps through a structured 52-week practical curriculum.
Program fee shown after the available discount. Ask the team about current batch availability and payment options.
- 52 weeks of AI-integrated, project-based data science and ML training
- Python, SQL, EDA, statistics, mathematics, ML, deep learning and deployment
- FastAPI, Docker, GitHub and MLOps-focused final capstone
- Notes, video references, learning resources and revision guidance
- 12 months of internship opportunities and project-work pathway after training
Internship opportunities, project work, work-experience documentation and job recommendations depend on progress, eligibility, completion requirements and available opportunities.
Build projects that show the range of your data science skills.
Every major part of the curriculum creates evidence of your ability to work with files, data, models, evaluation and deployment.
Student File Analysis System
Read CSV and Excel files, handle errors, clean records and create reports with Python.
Customer Orders Analytics
Combine MySQL data, analytical queries and Python reports to answer customer and sales questions.
Business Insight Notebook
Use Pandas, Matplotlib and Seaborn to uncover patterns and explain useful findings clearly.
Price Prediction Model
Compare multiple models, measure performance and justify a final model for house or used-car prices.
Churn Prediction System
Handle imbalanced data, evaluate business-relevant metrics and build a classification project.
Forecasting Mini Project
Explore time-based patterns and create an initial forecasting-oriented analysis.
FastAPI Model Service
Expose a trained model through an API and document how the prediction service works.
Dockerised ML Project
Package a final ML project with code, API, documentation, Docker and a GitHub repository.
More than a syllabus. Support to practise, revise and present your work.
Each resource is designed to help you keep learning consistently and show your growing technical ability more clearly.
Practical Coding Labs
Work through files, databases, notebooks, models and deployment tasks instead of isolated theory.
Project Completion Support
Build projects in stages with guidance on code, evaluation, documentation and presentation.
Notes & Video References
Use learning notes, curated references and revision material after each live session.
GitHub Portfolio Guidance
Organise repositories, README files and project structure so your work is easier to review.
Resume Templates
Present data science skills, models, projects and tools more clearly for relevant roles.
Interview Preparation
Prepare to explain data choices, model evaluation, business outcomes and your project workflow.
Industry Exposure
Understand how data teams approach projects, model quality, deployment and collaboration.
Internship Opportunities
Explore additional practical exposure after successful learning progress and program completion.
Job Recommendations
Receive role guidance and job recommendations based on readiness and available opportunities.
Learn from anywhere. Build serious technical skills.
Live online sessions make this Data Science and Machine Learning training program accessible across India. Agra-based learners can also attend in-house sessions.
Learn data science from home, college or your current city.
Join live training, practise with datasets, build notebooks and projects, and receive structured guidance without relocating.
- Live interactive coding and concept sessions
- Guided Python, SQL and ML practice
- Practical assignments and project support
- Useful for students, job seekers and working learners
Choose a classroom environment for in-person technical practice.
Attend in-house sessions at Learn2Earn Labs, work through project challenges with mentors and learn alongside other focused students.
- Face-to-face mentorship and doubt resolution
- Structured classroom learning environment
- Hands-on notebook, SQL and model-building practice
- Local career guidance and project support
Build options beyond a certificate.
As your project portfolio, technical understanding and professional confidence grow, you can prepare for data, analytics and entry-level machine learning opportunities.
Get clear before you start.
Who can join this Data Science and Machine Learning training program?
This program is suitable for college students, graduates, job seekers, career switchers and working professionals who want a structured path into data science and machine learning. Basic computer familiarity is enough to start; consistent practice is essential.
Do I need to know Python before joining?
No. The program begins with Python revision and core programming concepts, then progressively moves into file handling, data libraries, SQL, statistics, machine learning and deployment.
Can I join from another city?
Yes. Live online training is designed for learners across India. You can join from home, college, hostel or your current city. Offline training is also available for learners who wish to attend at the Agra centre.
Is this program focused on practical work?
Yes. Every four-week block includes a practical outcome, from file analysis and SQL projects to EDA, A/B testing, regression, classification and a deployed final capstone.
Which AI tools will I learn to use?
You will use ChatGPT, Claude, Gemini and Perplexity for research, code explanation, debugging, experiment planning, documentation and technical learning. You will also learn why all AI output must be checked against the data and trusted documentation.
Will I learn deep learning, deployment and MLOps?
Yes. The final advanced unit covers deep learning foundations, time-series basics, FastAPI, Docker, deployment, GitHub documentation and MLOps fundamentals as part of the capstone path.
Will I receive notes and learning resources?
Yes. The training includes notes, learning content, video references and revision resources to support continuous practice outside the live classes.
Are internship opportunities and career support available?
Yes. The program includes internship opportunities, project work, work-experience guidance, resume templates, interview preparation and job recommendations. These depend on progress, eligibility, completion requirements and available opportunities.
Is placement guaranteed after the program?
No responsible training provider can guarantee placement. This program is designed to build job readiness through practical data skills, complete projects, AI-assisted workflow, deployment exposure and career preparation.
⭕ Take the next step
Build the skills to turn data into deployed solutions.
Get the complete 52-week syllabus, current batch details, fee information, learning-mode guidance and a clear view of how this program fits your career goal.