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Machine Learning Bootcamp

  • Course Code: Artificial Intelligence - Machine Learning Bootcamp
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Data Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In Machine Learning you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. 

Working in a hands-on learning environment, led by Machine Learning expert instructor, students will learn about and explore: 

  • you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects 
  • you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice 
  • you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms.  
  • You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Code fundamental ML algorithms from scratch 
  • Collect and clean data for training models 
  • Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow 
  • Apply ML to complex datasets with images and text 
  • Deploy ML models to a production-ready environment 

Audience & Pre-Requisites 

This course is for readers want to learn the essentials of machine learning by completing a carefully designed set of real-world projects. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 
  •  No previous machine learning experience required 

Course Agenda / Topics 

  1.  
  1. Introduction to machine learning 
  • Machine learning 
  • Machine learning process 
  • Modeling and model validation 
  1. Machine learning for regression 
  • Car-price prediction project 
  • Exploratory data analysis 
  • Machine learning for regression 
  • Predicting the price 
  1. Machine learning for classification 
  • Churn prediction project 
  • Feature engineering 
  • Machine learning for classification 
  1. Evaluation metrics for classification 
  • Evaluation metrics 
  • Confusion table 
  • ROC curve and AUC score 
  • Parameter tuning 
  1. Deploying machine learning models 
  • Churn prediction model 
  • Model serving 
  • Managing dependencies 
  • Deployment 
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