AI & Machine Learning with Data Science Certification

Become an expert in AI, Machine Learning, and Data Science with hands-on projects, real-world applications, and globally recognized certification — designed to launch high-impact careers in tech.

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Unlock the power of AI, Machine Learning, and Data Science with our expert-led, job-focused program.
Master top tools like Python, TensorFlow, SQL, and Power BI, and gain hands-on experience with real-world projects.
Learn to build predictive models, generative AI systems, and deploy scalable ML solutions.
Benefit from personalized 1:1 mentorship, 24/7 support, and 100% placement assistance.
Step into the most in-demand tech roles with a portfolio that sets you apart in today’s data-driven world.

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Key Highlights

60+ live interactive session over the course of 6 months

Exclusive access to our Career Portal

170+ hrs of Live interactive Sessions

Live sessions with NIT Faculy and Industry Expert

24/7 Support

Industry-Curated Curriculum

50+ Industry level Projects & Assesments

100% Job Assurance*

Course content

Module 1: Foundations for Data Science, AI, and Data Exploration

What is Data?
Types of Data
Importance of Data Analysis and AI
Data Analysis Process and AI Pipeline
Ethical Considerations in Data Handling
AI Concepts and Overview
Introduction to Data Exploration Techniques (Basic Visualization)
Introduction to SQL (Basic Queries)
Overview of Excel for Data Handling

Module 2: Data Acquisition, Preparation, and Advanced Exploration

Python Programming Fundamentals (Review)
SQL Fundamentals: Basic Queries and Data Manipulation
Excel Fundamentals: Data Entry, Formulas, and Functions
Advanced Excel: PivotTables, Charts, and Advanced Functions
Data Visualization Techniques (Introduction)
Data Acquisition Strategies and APIs

Module 3: Statistical Analysis and Machine Learning Foundations

Descriptive Statistics
Probability Distributions
Hypothesis Testing
Linear Regression
Logistic Regression
Model Evaluation Metrics
Cross-Validation
Hyperparameter Tuning

Module 4: Supervised Learning Algorithms and Applications

Decision Trees and Random Forests
Support Vector Machines (SVMs)
K-Nearest Neighbors (KNN)
Ensemble Methods and Boosting
Bias-Variance Tradeoff

Module 5: Unsupervised Learning and Dimensionality Reduction

Clustering Algorithms (K-Means, Hierarchical, DBSCAN)
Dimensionality Reduction (PCA, t-SNE)
Anomaly Detection Techniques
Association Rule Mining

Module 6: Feature Engineering and Model Selection

Feature Scaling and Transformation
Feature Selection Methods
Handling Categorical Data
Model Selection Techniques
Pipelines and Automation
ML Ops Introduction

Module 7: Deep Learning Fundamentals

Neural Networks Basics
Activation Functions & Loss Functions
Optimization Algorithms
Regularization Techniques
Convolutional Neural Networks (CNNs) Overview
Recurrent Neural Networks (RNNs) Overview

Module 8: Generative AI Models and Architectures

Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Transformer Networks
Attention Mechanisms
Generative AI Use Cases

Module 9: Responsible AI, Deployment, and Scaling

Bias Detection and Mitigation
Explainable AI (XAI) Techniques
Privacy-Preserving Techniques
Model Deployment Strategies
Model Monitoring and Maintenance
MLOps Best Practices
Introdction to Docker and its implmentations

Module 10: Advanced AI Topics

Reinforcement Learning Overview
Transfer Learning
Meta-Learning

Module 9: Big Data Essentials for Data Science

Introduction to Big Data :
- Defining Big Data and its key characteristics (Volume, Velocity, Variety).
- The need for specialized tools and techniques for Big Data.
- Overview of common Big Data use cases in Data Science.
- Core Big Data Concepts and Architectures :
- Distributed Storage: Introduction to Hadoop
- Distributed File System (HDFS) - concepts of
- distributed storage.
- Distributed Processing:
- Introduction to MapReduce (basic idea).
Introduction to Apache Spark:
- Core concepts, advantages for data science.
- Overview of NoSQL databases and their relevance to Big Data (key-value, document stores).
Essential Big Data Tools for Analytics :
- Apache Spark: Focus on Spark DataFrames for data manipulation and analysis (using Python with PySpark). Basic operations and integration with Pandas.
- Introduction to Cloud-Based Big Data Services: Awareness of services like AWS S3/EMR, Azure Blob Storage/HDInsight, Google Cloud Storage/Dataproc. Understanding their basic function for storage and processing.
- Brief overview of Hadoop/Hive for data querying (if time permits).
Big Data Integration with Data Science :
- Using Spark to preprocess large datasets for Machine Learning.
- Understanding the scalability aspect of Machine Learning with Big Data tools (briefly).
  Visualizing Big Data (overview of challenges and tools).
Managing Big Data - Key Considerations :
- Brief introduction to data governance, security, and data quality challenges in Big Data environments.
  
  Tools Used:
  
  Apache Spark (with PySpark): For distributed data processing and analytics using Python.
  Cloud-Based Big Data Services (Conceptual): Awareness of AWS S3/EMR, Azure Blob Storage/HDInsight, Google Cloud Storage/Dataproc.
  Hadoop/HDFS (Conceptual): Basic understanding of distributed storage.
  Mongo db: A brief introduction to a NoSQL database like MongoDB11

Skills to Master

Data Cleaning and Preprocessing

Statistical Analysis

Machine Learning Model Building

Deep Learning

Generative AI Techniques

Model Evalutation and Selection

Feature Engineering

Dimensionality Reduction

Model Deployment and Scaling

Data Visualization

Communication and Data Storyteling

Ethical AI practices

SQL and Data Exrtractions

Tools to master

Roles You’ll Be Qualified For

Artifical Intelligence / Machine Learning Engineer

Natural Language Processing (NLP) Engineer

Data Scientist

AI Product Specialist/Engineer

Recommendation Systems Engineer

AI Solutions Architect

Machine Learning Operations (MLOps) Engineer

Prompt Engineer

Career Services

Mock Interview Preparation

Priority Access to Career Services

1 : 1 Career Mentorship

Job Board -- Resume Building

100% Placement Assurance*

Soft-Skill Training

Portfolio Building

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Certificate of Completion

🎓 Certification Sample
Get a glimpse of the professional certificate you’ll receive upon course completion. Issued by Workpreneur Academy, it validates your expertise in AI, Machine Learning, and Data Science, and showcases your skills to employers worldwide.

Testimonials from Industry Leaders

Slide 1 of 4

The AI/ML program at Workpreneur Academy helped me future-proof my career. The instructors made even complex topics like neural networks and deep learning approachable. I’ve since transitioned into an AI-focused role with more responsibilities and better pay.

- Amit Rao

This course bridged the gap between academic theory and industry application. I built predictive models, deployed machine learning pipelines, and even explored generative AI. I left with not just knowledge—but a portfolio that wowed recruiters.

- Karan Mehta

Transitioning into tech felt overwhelming until I joined Workpreneur Academy’s Data Science & AI/ML course. The structured learning, hands-on projects, and real-world case studies helped me land my first data analyst role within months. I’m finally doing work I love!

- Rajesh Kumar

I joined to explore how AI could be applied to my field—and I’m amazed. From data cleaning to deploying intelligent models, this course gave me practical tools I could bring into my research. The mentorship was phenomenal too.

- Arun

The AI/ML program at Workpreneur Academy helped me future-proof my career. The instructors made even complex topics like neural networks and deep learning approachable. I’ve since transitioned into an AI-focused role with more responsibilities and better pay.

- Amit Rao

This course bridged the gap between academic theory and industry application. I built predictive models, deployed machine learning pipelines, and even explored generative AI. I left with not just knowledge—but a portfolio that wowed recruiters.

- Karan Mehta

Transitioning into tech felt overwhelming until I joined Workpreneur Academy’s Data Science & AI/ML course. The structured learning, hands-on projects, and real-world case studies helped me land my first data analyst role within months. I’m finally doing work I love!

- Rajesh Kumar

I joined to explore how AI could be applied to my field—and I’m amazed. From data cleaning to deploying intelligent models, this course gave me practical tools I could bring into my research. The mentorship was phenomenal too.

- Arun

The AI/ML program at Workpreneur Academy helped me future-proof my career. The instructors made even complex topics like neural networks and deep learning approachable. I’ve since transitioned into an AI-focused role with more responsibilities and better pay.

- Amit Rao

This course bridged the gap between academic theory and industry application. I built predictive models, deployed machine learning pipelines, and even explored generative AI. I left with not just knowledge—but a portfolio that wowed recruiters.

- Karan Mehta

Transitioning into tech felt overwhelming until I joined Workpreneur Academy’s Data Science & AI/ML course. The structured learning, hands-on projects, and real-world case studies helped me land my first data analyst role within months. I’m finally doing work I love!

- Rajesh Kumar

I joined to explore how AI could be applied to my field—and I’m amazed. From data cleaning to deploying intelligent models, this course gave me practical tools I could bring into my research. The mentorship was phenomenal too.

- Arun

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Course Queries Answered

Who is this AI/ML with Data Science course designed for?

This program is ideal for aspiring data scientists, analysts, software engineers, and professionals looking to pivot into AI and ML roles. A basic understanding of programming and mathematics is helpful, but the course starts with foundational modules to get everyone up to speed.

What skills will I gain from this course?

You’ll master end-to-end data science workflows—from data cleaning, statistical analysis, and machine learning to deep learning, generative AI, and responsible AI deployment. You'll also gain hands-on experience with tools like Python, SQL, TensorFlow, PyTorch, and more.

What is the structure and duration of the program?

This is an 24-week intensive, step-by-step program. Each week covers a progressive topic, starting from AI and data science foundations to advanced subjects like Generative AI, Reinforcement Learning, and MLOps. It includes real-world projects, collaborative exercises, and case studies.

Will I receive a certificate and job support after completion?

Yes! Upon successfully completing the course, you'll receive a certification that’s shareable on LinkedIn and job applications. The course also includes 100% placement assistance, resume building, and mock interviews to help you land your dream role.

What kind of support is provided during the course?

You’ll have 24/7 support, 1:1 personalized mentorship, Study Material, Recordings with industry experts like G. Tanuj (Lead Generative AI/ML Engineer), and live sessions for doubt clearing, feedback, and career guidance.