Intro to Machine Learning and Deep Learning for Beginners

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(Course eligible for SATVs)

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Machine learning techniques are divided to classic machine learning techniques and contemporary ones (deep learning). These artificial Intelligence techniques are used to learn and infer from data and build computational models. Every day, we live with Machine Learning algorithms intertwining with our daily lives: Spam Filtering; Web Search; Postal Mail Routing; Fraud Detection; Movie Recommendations; Vehicle Driver Assistance; Web Advertisements; Social Networks and Speech Recognition. So it is important to know what are these techniques and when do we need them to apply?

You will be provided with the grand tour of machine learning and deep learning case scenarios for business. You will learn essential skills in Machine Learning and Deep Learning in Python as well as methodology for training machines. Included in this course is math for grown-ups, an introduction to programming Python. It also includes instructions on how to manage, visualize and analyze your data as well as providing lessons on building, validating and tuning your models. In addition to all the theory there will be a lot practice in the form of several exercise projects. By the end of this course, you will be familiar with the business opportunity of machine learning to unlock immense value in every day technology breakthroughs.

Students will also be given hands on experience as well through being taught the different types of problem-solving scenarios for Machine Learning and several examples of Deep Learning, including:

1. Overview of Machine Learning

  • Supervised and Unsupervised Learning

2. Overview of Classic Machine Learning Techniques

  • Use Cases for Machine Learning
  • Tools for Machine Learning

3. Overview of Deep Learning Techniques

  • Use Cases for Deep Learning
  • Data for Deep Learning
  • Tools for Deep Learning
  • Computer Visions Models (CNNs)

 

This is a Premium New Horizons course. This course usually runs in a connected classroom, and students need to bring their own device.

Lessons:

  • A quick introduction to data science and machine learning
  • What is the difference between supervised and unsupervised learning
  • Examples of applications of ML in Business

Lessons:

  • Decision Tree classification (concept )
  • Neural Network (concept)
  • Random forest classifucation (Concept)
  • Evaluating classification performance

Lessons:

  • A quick intro to the benefits of Deep Learning vs traditional Data Science Models
  • Overview of Deep Learning algorithms
  • Convolutional Neural Networks (CNN) (concept)
  • How to prepare data for CNN

Lessons:

  • Decision Tree implementation in Python
  • Neural Network implementation in Python
  • Random Forest implementation in Python
  • CNN implementation in Python

Lessons:

  • How to export the built model in Python
  • How to deploy the exported model on cloud using Azure ML Studio
  • How to run the model on premises

After completing this course, students will be able to:

  • Use Machine Learning and Deep Learning in the Enterprise using a variety of technologies
  • Python essentials
  • Use Azure ML Studio to deploy your model
  • How to do Machine Learning in On Premises
  • Methodology

Students will also have the knowledge to:

  • Conduct supervised Learning situations
  • Understand how to get started with Machine Learning and Deep Learning
  • Understand the situations where classic Machine Learning or Deep Learning techniques are best suited
  • Co-ordinate Deep Learning situations
  • Have enough experience with some of the Machine Learning and Deep Learning tools and how to go about setting up your own development environments when you return to your office
  • Recognize the role of the cloud and how edge computing differentiates your business from the competition, especially the role of edge computer vision in your organization
  • Recognize the expertise required to fully drive machine learning and deep learning in your organization
  • Know what the next steps are to unlock Machine Learning and Deep Learning and become part of the community

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