Course description

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 will you be able to

  • 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


Mathematics Refresher

  • 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

Machine Learning

  • Section 1: Stages in an ML project
    • Problem Definition: supervised vs. unsupervised learning
    • Research 
    • Exploratory Data Analysis
    • Data Aggregation/Mining/Scraping
    • Data Preparation/Preprocessing
    • Modelling
    • Training and evaluation
    • Deployment

    Section 2: Data Preprocessing

    • Data Import (Pandas, NumPy)
    • Handling missing values
    • Categorical data encoding
    • Outlier detection
    • Datetime processing (time series analysis)
    • Dataset splitting
    • Cross-Validation (Overfitting/Underfitting)
    • Feature Scaling: standardization and normalization
    • PCA + Theory

    Section 3: Confusion Matrix and Model Performance metrics

    • Recall
    • Precision
    • Accuracy
    • Root Mean Square Error
    • F1 score
    • ROC AUC
    • Confusion Matrix
    • Gini Coefficient

    Section 4: Regression Algorithms (with a Matrix notation):

    • Linear regression + Theory
    • Logistic regression + Theory
    • Regularization techniques (Lasso, Ridge)
    • Knn

    Section 5: Decision Tree Algorithms:

    • CART + Theory
    • Bagging & Boosting + theory
    • Random Forest + theory

    Section 6: Boosting Algorithms:

    • AdaBoost
    • XGBoost + Theory
    • LightGBM
    • CatBoost

    Section 7: Neural Networks

    • Neural Networks, backpropagation, and forward propagation (single-layer perceptron)
    • Multilayer perceptron
    • Cost functions
    • Activation functions

    Section 8: Optimization Methods (Hyper-parameters, Gradient Descent, etc.)

    • Gradient Descent
    • Stochastic Gradient Descent
    • Vanishing/exploding gradients

    Section 9: Hyperparameter optimization

    • Grid search
    • Random search
    • Bayesian optimization

    Section 10: Imbalanced classification

    • ROC curve and threshold choice
    • SMOTE (Oversampling)
    • Near-Miss (Undersampling)

    Section 11: Model Explainability

    • Permutation importance
    • Partial dependency plots
    • SHAP values

    Section 12: Unsupervised Learning:

    • K-Means clustering + Theory
    • Clustering evaluation metrics
    • Dimensionality reduction (e.g. t-SNE, UMAP)

Deep Learning

Section 1: DL Frameworks (Tensorflow2, Keras)

  • Introduction to Tensors and variables
  • Introduction to Gradients and Automatic Differentiation (tf.GradientTape())
  • Introduction to graphs and functions
  • Introduction to modules, layers, and models
  • Basic training loops
  • Advanced Automatic Differentiation
  • Keras Sequential model
  • Training & evaluation with built-in methods
  • Save and load Keras models
  • Working with preprocessing layers

Section 2: Working with Layers

  • Dropout and weight initialization
  • Optimization algorithms (Mini-batch gradient descent, Momentum, RMSProp, Adam)
  • Batch normalization

Section 3: CNN (Convolutional Neural Network)

  • Convolutions
  • Pooling
  • Fully connected layers

Section 4: RNN (Recurrent Neural Networks)

  • RNN
  • LSTM
  • GRU

Section 5: NLP

  • Classical methods overview (language model, tf-idf vectorization)
  • Word embeddings, CBOW and skip-gram methods, word2vec

Section 6: Time series

  • Time series analysis and transformation
  • Time series forecasting methods overview

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 program?

Roman Sitnichenko

Roman Sitnichenko

career manager

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