Course

Machine Learning - April 2025

Machine Learning is the intersection of programming with mathematical and scientific methods. It is the science of how humans can make computers provide solutions without being specifically programmed first. In this course, participants will get acquainted with the basics thanks to which all machine learning algorithms work. Those who have signed up will look at the main types of tasks to which any real problem can be reduced. For each task, you will get acquainted with several algorithms that solve it, and for each algorithm they will understand when it is used, how it is trained and tested. During the training, a number of practical tasks will be solved using the Python language – one of the most popular languages for the purposes of science and working with data. Training, on its own, or in combination with knowledge of mathematics and data science, is a starting point for career development: for positions such as Machine Learning Engineer, Applied Machine Learning Researcher, Data Scientist or similar.

Topics

Introduction to Machine Learning
Introduction to Machine Learning

• What is machine learning?;
• The scientific method;
• Basic principles: supervised and unsupervised learning, reinforcement learning;
• Preparing data;
• Preview of the entire process.
Resources
Linear and Logistic Regression
Linear and Logistic Regression

• Regression – problem statement and motivation;
• Ordinary least squares: method, simulated example, implementation on real data;
• RANSAC – robust regression model;
• Linear regression extensions: polynomial regression, multi-dimensional linear regression;
• Classification – problem statement and motivation;
• Logistic regression: method, simulated example, real data;
• Regularization for regression and classification.
Resources
Model Training and Improvement
Model Training and Improvement

• Training and testing set;
• Bias-variance tradeoff;
• k-fold cross-validation;
• Graphical methods: train/test curve, ROC, confusion matrix;
• Hyperparameters. Hyperparameter optimization;
• Model selection.
Resources
Tree and Ensemble Methods
Tree and Ensemble Methods

• Decision trees: information gain and entropy;
• Random forests;
• Boosting, AdaBoost;
• kNN (k nearest neighbors);
• Applications: regression and classification.
Resources
Support Vector Machines
Support Vector Machines

• SVM intuition: maximum margin;
• Kernels. "Kernel trick";
• Applications: regression and classification.
Resources
Clustering
Clustering

• Unsupervised learning: problem statement, intuition, challenges;
• k-means clustering – motivation, example, kMeans++;
• Hierarchical clustering – motivation, example;
• Comparison between k-means and HC: pros and cons;
• DBSCAN.
Resources
Dimensionality Reduction
Dimensionality Reduction

• Problem statement, intuition;
• Feature selection vs. feature extraction;
• Low variance filter, high correlation filter;
• Random forests as DR algorithms;
• Principal Component Analysis (PCA): motivation, example, interpretation;
• Isometric mapping for DR and visualization.
Resources
Introduction to Neural Networks
Introduction to Neural Networks

• Problem statement, motivation and results. Pros and cons;
• NN representation;
• Perceptron, softmax function;
• Two-class and multi-class problems;
• Feed-forward NNs: forward propagation;
• Chain rule. Backpropagation and learning;
• Regularization. Cross-entropy cost function;
• NN examples with real data;
• Convolutional NNs: ideas, example
Resources
Time Series
Time Series

1.Time Series
§Exploration
§Feature extraction
§Feature engineering
2.Modelling
§Data preparation
§Traditional models
§Neural networks
Resources
- Lecture
- Video - Record
ML in Production Demos
ML in Production Demos

No description yet
Resources
- Material
- Video - Record

Who is the target audience for the course?

The course is suitable for people with basic programming knowledge (variables, control constructs, lists, functions) and knowledge of mathematics at grade 12 level. It is desirable that students have knowledge of Python and elements of "higher" mathematics, such as the basics of statistics and mathematical analysis. The required level of English required for the course must meet B2.

How to enroll in the course?

To sign up for the course, contact MLC Business School and follow the provided steps. After a successful payment, you will be enrolled in the training.

What is the deadline for enrolling in the course and when does it start?

Enrollment in the course is open until 05 February. The training starts on 27 March 2025. You can find a detailed schedule of classes in the Topics section.

How and where are the classes be held?

You can study online in real time. Immediately after each lesson, you also get access to the lesson recording and learning resources.

What is the date of the exam and what does it include?

The exam will be held online and includes practical exercises. The dates of the exam are May 03rd (Final Exam) and May 10th (Retake exam).

Do I get a certificate after the exam?

After passing the exam, you acquire a certificate issued by SoftUni if your score is above 70%.

How much is the course fee and what does it include?

Online

400 USD

Online training in real-time

Lifetime access to lesson recordings and learning content

Help from a mentor in understanding the learning materials

Access to a closed Facebook group with all other course participants

Taking a regular exam and receiving a certificate

SoftUni gives you a 100% guarantee of the quality of this course. The most important thing for us is that you acquire the necessary skills and knowledge. In the event that the training fails to fulfil your expectations, we guarantee a full refund of the amount you have paid. You can receive a refund until the fifth lesson of the course.