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Course Skill Level:

Foundational to Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Developers with basic Linux & computing skills and basic to intermediate MS Azure & ML experience seeking expert-level ML status

Who should attend & recommended skills

  • This course is geared for those with basic Linux and computing skills who want a one-stop guide to becoming a Machine Learning expert.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Microsoft Azure: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning: : Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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 will 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning instructor, students will learn about and explore:
  • Developing 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
  • Implementing Machine Learning in the most practical way
  • The math and mechanics of Machine Learning via a developer-friendly approach
  • Getting to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems
  • Getting a feel for advanced concepts, using popular programming frameworks.
  • Preparing yourself and other developers for working in the new ubiquitous field of Machine Learning
  • Getting an overview of the most well known and powerful tools, to solve computing problems using Machine Learning
  • Getting an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems.

Course breakdown / modules

  • Machine learning in the bigger picture
  • Tools of the trade programming language and libraries
  • Basic mathematical concepts

  • 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

  • Grouping as a human activity
  • Automating the clustering process
  • Finding a common center – K-means
  • Nearest neighbors
  • K-NN sample implementation

  • Regression analysis
  • Linear regression
  • Data exploration and linear regression in practice
  • Logistic regression

  • History of neural models
  • Implementing a simple function with a single-layer perceptron

  • Origin of convolutional neural networks
  • Deep neural networks
  • Deploying a deep neural network with Keras
  • Exploring a convolutional model with Quiver

  • Solving problems with order RNNs
  • LSTM
  • Univariate time series prediction with energy consumption data

  • GANs
  • Reinforcement learning
  • Basic RL techniques: Q-learning

  • Linux installation
  • macOS X environment installation
  • Windows installation