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AI Career Accelerator

Information

This course includes

​

  • 20 hours of LIVE classes​

  • 2 articles to help you onboard

  • ​5 downloadable resources

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Why Choose Our Program?

  • Intensive, hands-on labs with real-world projects.

  • Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.

  • AI portfolio Networking with industry leaders and peers

  • Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.

  • AI portfolio Networking with industry leaders and peers

What you'll learn:

  • Build generative AI systems with OpenAI, RAG, and LLM Agents
  • Build artificial neural networks with Tensorflow and Keras
  • Implement machine learning at massive scale with Apache Spark's MLLib
  • Classify images, data, and sentiments using deep learning
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Data Visualization with MatPlotLib and Seaborn
  • Understand reinforcement learning - and how to build a Pac-Man bot
  • ​Clean your input data to remove outliers
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

Model Explainability & Ethics

  • Explainable AI (XAI) Techniques

  • Fairness in Machine Learning

  • Privacy and Data Governance in ML

Capstone Project

  • Capstone Project Kickoff

  • Project Work Time

  • Final Presentations and

  • Feedback

Industry Applications & Next Steps

  • Industry Case Studies

  • Career Pathways in ML

  • Course Wrap-up and Reflection

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

🔗 Limited Spots Available

Know your Instructor:

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