Machine Learning Immersive

New York, 115W 30th St.
Machine Learning Immersive
3 Reviews
  • Timothy Peach
  • Beginner
    Skill Level

We’ve taken several steps to protect our associates and students: all seats are located 6 ft apart, people are encouraged to wear masks, and we sanitize our classrooms regularly.


In this course, you will learn the fundamentals of Machine Learning and reinforce these concepts by working with real data and building meaningful predictive models. Afterwards, you will have a portfolio where you can showcase your projects, and the necessary foundation in Machine Learning to continue in a direction that best fits your interests and skillset.


If you are aspiring programmer, whether beginner or jedi, this course is for new learners on the pursuit to master machine learning concepts. This course discusses the FUNDAMENTAL principles of machine learning. No prior machine learning experience necessary. Beyond doing your part as a hard working student, you walk away with skills that translate to the programming marketplace that can either cultivate your future programming career or give you a bump in your pre existing career by showing employers your new skillset. The decision on how hard you work lies with you. In this one week course, you will become involved in a decision-making process surrounding the usage of machine learning, how it can help achieve business and project goals, which machine learning techniques to use, potential pitfalls, and how to interpret the results. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.


Typical studying day starts at 10pm with a previous day recap and completing previous exercises. Lecture on new topics takes about two hours and starts at 11.00pm. After lecture, students start working on new exercises with instructor guidance. Around 3pm students present and discuss their work with instructors, learn alternative solutions, and best practices from instructors and invited professional programmers.


  • Regression: Linear Regression, Polynomial Regression, Backward Elimination of Regressors
  • Classification: Naive Bayes, Logistic Regression, Support Vector Machines.
  • Resampling, Bootstrapping, and Cross Validation
  • Regularization: Lasso and Ridge Regression
  • Dimension Reduction Trees: Decision Trees, Bagging, Boosting, Random Forest
  • Unsupervised Learning: K-Means Clustering,
  • Neural Networks: Intro To Artificial Neural Networks and Deep Learning
  • David Stern
    Mon, May 25

    This is the opportunity you have been waiting for. Pull the trigger now on an explainer video from the best in the business. Do it while we are running our “best pricing ever” campaign and save thousands of dollars. This campaign happens once in a blue moon. Well, that’s not exactly true, but it has never happened before - and I don’t suppose we will run this promotion again - hence the “best pricing ever” My name is David Stern and I look forward to working with you to create a game changing explainer and marketing video for you. Something that will be an unquestionable business asset. Email me back for some samples. -- David Stern Email: Website:

  • Julia
    Thu, Sep 19

    I really enjoyed the Machine Learning Immersive Course, it was informative and very well taught. It is helpful to do the Python Immersive course first to have some basic knowledge about python but it works also without experience. I can only recommend it :)

  • Indika
    Sun, Aug 04

    This five days course was a valuable experience for me. I really enjoyed the instructor, Mr. Tim and his instructions. He is very knowledgeable and very patient enough to help all the participants. Now I have a very good understanding about Machine Learning and this course made my curiosity high to learn more and more.

Things To Remember

  • Please bring your own laptop to class.
  • We will help you to install all the programs you need in class.
  • If you withdraw one day before the course start date, any deposit paid will be refunded in full.
  • If you cannot attend classes for which you were charged, you could join next cohort and make up the missed classes.