top of page
VR Goggles

Free Demo

AI Career Accelerator

What you'll learn:

  • Learn to integrate AI/ML techniques into traditional ETL pipelines to build intelligent data workflows. 

  • Gain proficiency in machine learning algorithms such as regression, classification, and clustering using Python libraries. 

  • Build a strong foundation in linear algebra and statistics tailored for real-world machine learning applications. 

  • Perform exploratory data analysis (EDA) using Matplotlib, Seaborn, and Plotly to uncover actionable insights. 

  • Apply advanced feature engineering techniques to enhance model input quality and improve performance. 

  • Design scalable training and testing pipelines that prevent data leakage and are ready for production deployment. 

  • Develop and validate ML models using frameworks like Scikit-Learn, TensorFlow, and PyTorch. 

  • Optimize model performance with evaluation metrics, hyperparameter tuning, and interpretability tools like SHAP and LIME. 

  • Deploy ML models using MLOps best practices, including continuous integration, monitoring, and drift detection. 

  • Explore deep learning applications in NLP, computer vision, and agentic AI with hands-on experience in transformers and LLMs. 

Why Choose Our Program?

Our guiding principle is clear and impactful: we deliver hands-on, end-to-end execution of four real-time, industry-standard projects across four different industries — from defining the problem statement to building ML models live in class, all the way through to deploying them into production.

  • Showcase your skills by adding a GitHub portfolio with these projects directly to your resume, demonstrating your practical ML/AI expertise.

  • Engage in intensive, hands-on labs designed around real-world challenges to build confidence and competence.

  • Benefit from our comprehensive Interview Strategy module, equipping you with the skills and preparation needed to successfully pivot into the AI career landscape.

Course Content:

Introduction to ML/AI for Data Engineers

  • Overview of ML/AI Concepts

  • Mapping Data Engineering to ML

  • Course Roadmap & Expectations

  • Weekly Mini Project

Fundamentals of ML, Linear Algebra and Statistics

  • ML Basics & Common Algorithms

  • Linear Algebra Essentials

  • Statistics & Hypothesis Testing

  • Integration and Wrap-up

EDA for ML

  • Data Visualization Techniques

  • Statistical Summaries & Descriptive Analytics

  • Outlier Detection & Data Quality Assessment

Feature Engineering for ML

  • Feature Extraction Techniques

  • Automated Feature Engineering Tools

  • Practical Feature Engineering Applications

  • Weekly Mini Project

Data Pipelines for ML

  • Designing ETL/ELT Processes for ML

  • Advanced Data Cleaning & Transformation

  • Integrating Real-Time Data Flows

  • Weekly Mini Project

Model Selection & Algorithm overview

  • Model Selection & Algorithm Overview

  • Hands-On Model Training

  • Best Practices in Training

Hyper-parameter Tuning and Model Evaluation

  • Hyper-parameter Tuning Techniques

  • Model Evaluation Metrics Part 1

  • Practical Evaluation Exercise

ML Ops & Model Deployment

  • Deployment Strategies & CI/CD for ML

  • Monitoring & Maintenance

  • Hands-On Deployment Exercise

  • Weekly Mini Project assignment & conclusion

Introduction to Deep Learning Concepts

  • Deep Learning Fundamentals

  • Building and Training a Simple Neural Network

  • Deep Learning vs. Traditional ML

  • Privacy and Data Governance in ML

Deep Learning Domain Applications

  • Introduction to Domain Applications

  • Case Studies and Practical Examples

  • Weekly Mini Project Introduction and Wrap-up

Specialized NLP, Transformers, LLM and GenAI

  • Advanced NLP Techniques

  • Transformers & Attention Mechanisms

  • Applications of Large Language Models

  • Weekly Mini-Project Introduction

Specialized NLP

  • NLP Fundamentals Recap

  • Sequence Modeling & Embedding

  • NLP with Transformers

  • Mini-Project Introduction

Agentic AI

  • Introduction to Agentic AI

  • Core Architectures & Planning Strategies

  • Agent Frameworks in Practice

  • Mini-Project Introduction

Interview Strategy

  • Behavioral Interview Preparation & Strategy

  • Resume Makeover with Expert Guidance

  • Professional Branding for AI/ML Roles

  • Live Mock Interviews with Feedback

Who Should Apply:

  • Engineers seeking to pivot into ML/AI roles.

  • Professionals aiming to deepen their AI expertise.

  • Anyone passionate about leveraging AI in real-world scenarios

Modular Course Fee:

  • ML Technical + ML Ops Track + Gen AI + Agentic AI ($599) – Master core AI/ML skills

  • Behavioral Interview Prep ($199) – Ace soft skills & storytelling

  • Professional Resume Writing ($199) – Get a job-ready resume

  • FULL Program ($999) – End-to-end career support (Best value!)

Go to our pricing page to checkout

Limited Spots Available. Contact us on whatsapp for more details

whatsapp2.png

AI Career Accelerator

4 Plans Available, From $199.00
bottom of page