201 - Enterprise Machine Learning

Scaling advanced analysis techniques to enterprise production levels, using Amazon Web Services (AWS) and other tools to automate scripts, analysis, and report workflows to maximize your impact and minimize your workload.

  2. Courses
  3. 201 - Enterprise Machine Learning
Sheung Wan, Hong Kong
6 weeks
12 people
Price: $18,000 HKD
Discount: $3,000 HKD for 101 graduates
Dhruv Sahi

Instructor

Dhruv Sahi
Related Courses

101 - Machine Learning Foundations

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Course Description

Scaling advanced analysis techniques to enterprise production levels, using Amazon Web Services (AWS) and other tools to automate scripts, analysis, and report workflows to maximize your impact and minimize your workload.

Philosophy

We focus on the practical, not theoretical side of Data Science and Machine Learning. Our courses are tailored around teaching students how to extract insight from data using Machine Learning & AI without focusing too much on the Mathematics or mechanics behind it. Our courses are focused on practical learning with use cases and data sets from economics, social policy, ecommerce, marketing, and more. Reboot creates future economy ready employees by covering three core skill sets from the full data solutions pipeline. If you've ever been frustrated with the theoretical nature of other courses in ML and AI, then this is the course for you.

Prerequisites

You should be proficient with the basic concepts of programming in at least one language (not necessarily Python), and should possess at least a high level familiarity with basic concepts in statistics, linear algebra, and machine learning.

Curriculum

  • Week 1 - AWS and Scaled Architecture

    Production level landscape, Amazon Web Services set up, and using AWS to automate data retrieval and cleaning scripts

  • Week 2 - Enterprise Analysis

    Creating interactive, multilayered dashboards, and scalable, production level analysis systems such as recommendation engines

  • Week 3 - Deep Learning

    Unsupervised vs supervised learning, and how to combine unsupervised techniques with neural networks for advanced insights on raw data

  • Week 4 - Artificial Intelligence

    Neural networks, Natural Language Processing, image recognition, and using Tensorflow for scaled 'intelligent' applications

  • Week 5 - Time Series and Advanced Analysis

    Modeling and predicting time series such as stock prices, and combining ML techniques to optimize results

  • Week 6 - Enterprise Level Portfolio Project

    Run an entire production level project from start to finish- use advanced ML techniques deployed on AWS' cloud based platforms to generate rich, interactive dashboards with immediately relevant insights

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Students got a lot of value and I learned a few tricks that can help with further development of my skills. Students who complete both the intro and advanced courses have a set of practical skills to be a good candidate for an intro data scientist.
Michal S.
Head of Analytics and Data Science
GOGOVAN

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