AI Career Accelerator
What you'll learn:
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Learn to integrate AI/ML techniques into traditional ETL pipelines to build intelligent data workflows.
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Gain proficiency in machine learning algorithms such as regression, classification, and clustering using Python libraries.
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Build a strong foundation in linear algebra and statistics tailored for real-world machine learning applications.
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Perform exploratory data analysis (EDA) using Matplotlib, Seaborn, and Plotly to uncover actionable insights.
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Apply advanced feature engineering techniques to enhance model input quality and improve performance.
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Design scalable training and testing pipelines that prevent data leakage and are ready for production deployment.
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Develop and validate ML models using frameworks like Scikit-Learn, TensorFlow, and PyTorch.
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Optimize model performance with evaluation metrics, hyperparameter tuning, and interpretability tools like SHAP and LIME.
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Deploy ML models using MLOps best practices, including continuous integration, monitoring, and drift detection.
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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.
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Showcase your skills by adding a GitHub portfolio with these projects directly to your resume, demonstrating your practical ML/AI expertise.
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Engage in intensive, hands-on labs designed around real-world challenges to build confidence and competence.
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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
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Overview of ML/AI Concepts
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Mapping Data Engineering to ML
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Course Roadmap & Expectations
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Weekly Mini Project
Fundamentals of ML, Linear Algebra and Statistics
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ML Basics & Common Algorithms
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Linear Algebra Essentials
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Statistics & Hypothesis Testing
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Integration and Wrap-up
EDA for ML
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Data Visualization Techniques
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Statistical Summaries & Descriptive Analytics
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Outlier Detection & Data Quality Assessment
Feature Engineering for ML
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Feature Extraction Techniques
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Automated Feature Engineering Tools
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Practical Feature Engineering Applications
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Weekly Mini Project
Data Pipelines for ML
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Designing ETL/ELT Processes for ML
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Advanced Data Cleaning & Transformation
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Integrating Real-Time Data Flows
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Weekly Mini Project
Model Selection & Algorithm overview
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Model Selection & Algorithm Overview
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Hands-On Model Training
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Best Practices in Training
Hyper-parameter Tuning and Model Evaluation
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Hyper-parameter Tuning Techniques
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Model Evaluation Metrics Part 1
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Practical Evaluation Exercise
ML Ops & Model Deployment
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Deployment Strategies & CI/CD for ML
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Monitoring & Maintenance
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Hands-On Deployment Exercise
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Weekly Mini Project assignment & conclusion
Introduction to Deep Learning Concepts
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Deep Learning Fundamentals
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Building and Training a Simple Neural Network
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Deep Learning vs. Traditional ML
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Privacy and Data Governance in ML
Deep Learning Domain Applications
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Introduction to Domain Applications
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Case Studies and Practical Examples
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Weekly Mini Project Introduction and Wrap-up
Specialized NLP, Transformers, LLM and GenAI
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Advanced NLP Techniques
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Transformers & Attention Mechanisms
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Applications of Large Language Models
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Weekly Mini-Project Introduction
Specialized NLP
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NLP Fundamentals Recap
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Sequence Modeling & Embedding
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NLP with Transformers
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Mini-Project Introduction
Agentic AI
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Introduction to Agentic AI
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Core Architectures & Planning Strategies
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Agent Frameworks in Practice
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Mini-Project Introduction
Interview Strategy
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Behavioral Interview Preparation & Strategy
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Resume Makeover with Expert Guidance
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Professional Branding for AI/ML Roles
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Live Mock Interviews with Feedback
Who Should Apply:
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Engineers seeking to pivot into ML/AI roles.
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Professionals aiming to deepen their AI expertise.
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Anyone passionate about leveraging AI in real-world scenarios
Modular Course Fee:
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ML Technical + ML Ops Track + Gen AI + Agentic AI ($599) – Master core AI/ML skills
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Behavioral Interview Prep ($199) – Ace soft skills & storytelling
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Professional Resume Writing ($199) – Get a job-ready resume
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FULL Program ($999) – End-to-end career support (Best value!)