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Machine Learning for Developers

  • Course Code: Artificial Intelligence - Machine Learning for Developers
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for those who wants one-stop guide to becoming a Machine Learning expert.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants one-stop guide to becoming a Machine Learning expert. 
  • 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. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The course will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the course, you will have learned various ML techniques to develop more efficient and intelligent applications. 

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

  • Learn to develop efficient and intelligent applications by leveraging the power of Machine Learning 
  • A highly practical guide explaining the concepts of problem solving in the easiest possible manner 
  • Implement Machine Learning in the most practical way 

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

  • Learn the math and mechanics of Machine Learning via a developer-friendly approach 
  • Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems 
  • Get a feel for advanced concepts, using popular programming frameworks. 
  • Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning 
  • Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning. 
  • Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems. 

Audience & Pre-Requisites 

This course is geared for attendees with basic Linux and computing skills who wish to Gain one-stop guide to becoming a Machine Learning expert. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills, Microsoft azure and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction – Machine Learning and Statistical Science 
  • Introduction – Machine Learning and Statistical Science 
  • Machine learning in the bigger picture 
  • Tools of the trade–programming language and libraries 
  • Basic mathematical concepts 
  1. The Learning Process 
  • The Learning Process 
  • Understanding the problem 
  • Dataset definition and retrieval 
  • Feature engineering 
  • Dataset preprocessing 
  • Model definition 
  • Loss function definition 
  • Model fitting and evaluation 
  • Model implementation and results interpretation 
  1. Clustering 
  • Clustering 
  • Grouping as a human activity 
  • Automating the clustering process 
  • Finding a common center – K-means 
  • Nearest neighbors 
  • K-NN sample implementation 
  1. Linear and Logistic Regression 
  • Linear and Logistic Regression 
  • Regression analysis 
  • Linear regression 
  • Data exploration and linear regression in practice 
  • Logistic regression 
  1. Neural Networks 
  • Neural Networks 
  • History of neural models 
  • Implementing a simple function with a single-layer perceptron 
  1. Convolutional Neural Networks 
  • Convolutional Neural Networks 
  • Origin of convolutional neural networks 
  • Deep neural networks 
  • Deploying a deep neural network with Keras 
  • Exploring a convolutional model with Quiver 
  1. Recurrent Neural Networks 
  • Recurrent Neural Networks 
  • Solving problems with order — RNNs 
  • LSTM 
  • Univariate time series prediction with energy consumption data 
  1. Recent Models and Developments 
  • Recent Models and Developments 
  • GANs 
  • Reinforcement learning 
  • Basic RL techniques: Q-learning 
  1. Software Installation and Configuration 
  • Software Installation and Configuration 
  • Linux installation 
  • macOS X environment installation 
  • Windows installation 
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