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      Machine Learning Immersive in New York

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      March 25, 2019

      Monday  10:00 AM

      115 West 30th Street , 5th Floor
      New York, New York

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      Machine Learning Immersive

      The demand for Data Analysts, Data Scientists, and Machine Learning Engineers continues to increase, as companies seek to harness the universal benefit of data-driven insights and predictive modeling.  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. READ SYLLABUS and CURRICULUM This is One week course, Monday to Friday from 10am to 5pm. Total in-Class Hours: 35 Mon, March 25th, 10.00am - 5.00pm Tue, March 26th, 10.00am - 5.00pm Wed, March 27th, 10.00am - 5.00pm Thu, March 28th, 10.00am - 5.00pm Fri, March 29th, 10.00am - 5.00pm The programming language we’ll be using is Python, the leading language for Machine Learning.  You should already have some experience coding in Python, but you don’t have to be a professional programmer.  If you’re completely new to Python, we offer a Python Immersive course which will give you the tools you need to get started with Machine Learning Immersive. On the first day, we’ll shore up our fundamentals in Python with a review, and then begin the dive into Machine Learning.   You will learn: Review of Python Fundamentals Python Libraries: Pandas, Sci-kit learn, and Matplotlib Supervised Learning Models (Regression and Classification) Unsupervised Models (Clustering) Data Cleaning and Standardizing the Data Exploratory Data Analysis Feature Engineering Modeling Fundamentals: Train/Test Split, Cross validation, Underfit and Overfit, the Bias-Variance Trade-off Interpreting the Results of Machine Learning Models Putting your projects on Github (can act as a portfolio)   Some of the projects for the session: Predicting types of flowers using the Iris dataset. Predicting hand-written digits using the MNIST image dataset. Predicting house prices with the Ames Housing dataset. Direction toward a project of your own that is ideally both interesting to you, and important. 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. 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. Prerequisites Laptop

      Categories: Science

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