About this course

Machine learning has become a hot topic in recent years as businesses around the world strive to remain competitive in a rapidly evolving market. A solid understanding of ML techniques enables you to add value to strategic business initiatives and boost KPIs by devising, implementing, and fine-tuning robust pattern-recognition models.  

Freshers in the Machine and Deep Learning course explore a variety of maths, statistics, and business-world problems as well as their cutting-edge solutions, building practical Big Data skills from the ground up. 

Eager to apply your new skillset on the job? You won’t have to wait long – as demand for job-ready ML specialists far outweighs supply.  

Machine Learning’s versatility and wide range of applications are what make it a heavily in-demand skillset. Some areas where ML is making waves are personalised marketing, FinTech, crisis and risk management, cybersecurity, precision medicine, robotics, and many more. 

Don’t let tall tales of the over-complexity of Machine Learning dissuade you from pursuing a fruitful career in the field! 

With this course, you will grasp the fundamentals of machine learning, gain the mathematical intuition needed to create predictive analytics models, and develop a comprehensive toolkit of application-ready ML algorithms.

Course Prerequisites 

  • 18+ Years of Age
  • Experience with Python’s NumPy, Pandas, and sklearn Libraries (recommended)
  • Intermediate (B2) Level of English
  • Advanced-Level Knowledge of Mathematics and Statistics
  • Personal Computer or Laptop
  • Grit and Readiness to Learn

Who is this course for?

  • Python programmers looking to advance into ML specialists
  • Data Analysts eager to upskill in the analytics tools of the future
  • Math graduates with the desire to break into the Data Science field

What you will learn in this course:

  • Master a variety of prominent ML algorithms (regression, decision trees, boost, etc.)
  • Complete business-world projects effectively by employing suitable models
  • Analyse a project and devise a viable solution
  • Pre-process and validate your data
  • Report on key model performance metrics (accuracy, precision, recall, etc.)
  • Apply supervised/unsupervised learning methods
  • Practice Deep Learning frameworks

Explore Curriculum

Module 1: Mathematics Primer

In this introductory module, you will go through core mathematical concepts that lay the foundation of machine learning, such as matrices, probability theory, and calculus.

  • Probability theory (main data distributions review) and Bayesian statistics
  • Functions theory
  • Matrix calculus (obtain derivative from a matrix)
  • Matrix decomposition techniques (SVD, LR)
  • Math operations with NumPy, efficient matrix operations, broadcasting, vectorization
  • KDE
  • Q-Q Plots

Module 2: Machine Learning

Master exploratory data analysis, model training, and evaluation, as well as hyperparameters optimisation. Grasp the theoretical foundations of powerful ML models and learn how to compare and select the most effective one for your needs. You will gain hands-on experience with cutting-edge tools, libraries, and platforms used in predictive analytics and machine learning.

  • Stages in an ML project
  • Data Pre-processing 
  • Confusion Matrix and Model Performance metrics
  • Regression Algorithms (with a Matrix notation)
  • Decision Tree Algorithms
  • Boosting Algorithms
  • Neural Networks
  • Optimisation Methods (Hyper-parameters, Gradient Descent, etc.)
  • Hyperparameter optimisation
  • Imbalanced classification
  • Model Explainability
  • Unsupervised Learning

Module 3: Deep Learning

In this deep dive into neural network concepts and architectures, you will get to practice working with all the most popular frameworks for creating, visualising, analysing, and debugging deep learning models.

  • DL Frameworks (Tensorflow2, Keras)
  • Working with Layers
  • CNN (Convolutional Neural Network)
  • RNN (Recurrent Neural Networks)
  • NLP
  • Time series analysis

Capstone Project

Your capstone project will consist of a real-world business use case, which you will explore under the supervision of an experienced mentor.

  • Practical industry use case
  • Participate in a Kaggle competition

Course instructors

  • Pavlo Chernega

    Pavlo Chernega

    Senior Machine Learning Engineer, Samsung Electronics

Flexible Tuition Fees

Don’t let money stop you from landing your dream tech job!

Find the payment option that works best for you.

  • Prepayment

    Get our best offer by paying for the entire course in one instalment as you enrol.

  • Half/Half

    Cover half of the course cost as you start and the other half after you complete your final project.

  • Monthly

    Pay a fixed monthly amount during the full run of your training.

  • Learn Now - Pay Later

    Start repaying your course fees only after you’ve found employment.

Do you have questions about the programme?

Roman Sitnichenko

Roman Sitnichenko

career manager

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