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Machine Learning

Machine Learning will offer participants a clear presentation and deep understanding on state-of-the-art machine learning concepts, ranging from regression, classification and clustering techniques to deep learning, adversarial learning, and reinforcement learning
Aimed At
Course Review
Why Choose this Course
You will learn
Course Outline
Training Format

Customer Tailored

We can tailor the included topics,tech level,and duration of this course right to your team’s technical requirements and needs. - MCNS offers courses to companies, institutions, departments etc and not to individuals as per open courses.
Aimed At

Machine Learning is a five-day course that is designed for technical professionals, including software engineers, data engineers, data scientists, machine learning engineers, machine learning scientists, and IT professionals.

 Prerequisites: Participants should have a relatively good understanding – at undergraduate level – of calculus, linear algebra, optimization, probability, and statistics. Participants are also expected to be familiar with coding in a high-level language (ideally Python) for the hands-on sessions.

Course Review

This Machine Learning training course leads the audience into a deep dive towards modern machine learning models, algorithms, tools, and applications.


The course content is split into five parts:

  • Part 1 introduces basic machine learning models and algorithms with a focus on supervised learning methods such as regression and classification
  • Part 2 introduces additional machine learning models and algorithms with a focus on unsupervised learning techniques such as clustering
  • Part 3 presents deep learning methods – including neural network models, convolutional neural networks, recurrent neural networks; it also presents adversarial learning methods such as GANs
  • Part 4 focuses on reinforcement learning, deep reinforcement learning, and their applications
  • Part 5 covers emerging topics in machine learning such as privacy, fairness, and explainability in machine learning and artificial intelligence

The course also offers – across these five parts – an overview of applications of the various machine learning approaches, in a variety of areas such as computer vision, speech recognition, speech translation, and natural language processing

Participants will be able to learn about, study and review modern aspects of machine learning; participants will also experiment with various data-oriented problems, challenges, and use-cases using Python and/or Jupyter notebooks.

Course Benefits for individuals (Professionals)
  • Understanding key machine learning principles, methods, and tools for supervised and unsupervised learning
  • Understanding cutting-edge machine learning approaches such as deep learning, reinforcement learning, adversarial learning, and other state-of-the-art techniques
  • Gaining a competitive advantage by exploring applications of cutting-edge machine learning and artificial intelligence techniques in computer vision, speech recognition, speech translation, and natural language processing
  • Diving into emerging topics in AI such as Privacy, Fairness, Transparency, Explainability, and Accountability
Course Benefits for your Organization
  • Equipping the engineers of your organization with the necessary knowledge to develop state-of-the-art machine learning models, algorithms, and methods
  • Preparing your team for future deployments of artificial intelligence by exposing your team to topics such as AI Privacy, Fairness, Transparency, and Ethics
  • Enhancing your team’s technical understanding of emerging applications of machine learning and artificial intelligence, to areas such as big data, computer vision, and natural language processing
  • Offering your team a view of real-world case studies to ensure that it is ready to recognize machine learning opportunities for your organization
You will learn
The key points you will learn through this course

The Elements of Machine Learning

Modern Developments in Machine Learning

Transparent AI

Course Outline
A short brief of your program details & schedule

Introduction to Machine Learning I

  • Introduction to supervised learning
  • Linear regression, polynomial regression, and logistic regression
  • Regularization techniques, and cross-validation
  • Learning algorithms: gradient descent, mini-batch and SGD
  • Support vector machines and kernels
  • Hands-on workshop in Python and/or Jupyter notebooks

Introduction to Machine Learning II

  • Introduction to unsupervised learning
  • Clustering: k-means, hierarchical, and spectral clustering
  • Density estimation: parametric and non-parametric
  • Feature reduction, selection, and transformation
  • Hands-on workshop in Python and/or Jupyter notebooks

Deep Learning

  • Introduction to neural networks: motivation, representation, and learning
  • Convolutional neural networks and applications in computer vision
  • Recurrent neural networks and applications in natural language proc.
  • Long short term memory (LSTM) networks
  • Transformers and applications
  • Adversarial learning and generative adversarial networks
  • Hands-on workshop in Python and/or Jupyter notebooks

Reinforcement Learning

  • Introduction to reinforcement learning
  • Exploration vs exploitation; practical solving and learning methods
  • Deep reinforcement learning
  • Deep reinforcement learning applications
  • Hands-on workshop in Python and/or Jupyter notebooks

Trustworthy Machine Learning

  • Introduction to trustworthy machine learning
  • Machine learning privacy
  • Machine learning fairness
  • Machine learning explainability
  • Federated learning
  • Hands-on workshop in Python and/or Jupyter notebooks
Training Format

Instructor-Led Training

On-Site Classroom: 5 days

Web delivered (Virtual): 5 days

Excellent and descriptive course material – pdf files and associated code (Jupyter notebooks) will be provided

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    Machine Learning

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