100% Booked
Machine Learning Academic Program

- Mathematical Analysis
- Linear Algebra
- Probability and Statistics
- Python Basics, Functions and Modules
- Machine Learning
- Deep Learning
100% Booked
When:
Days:
Tue 7:30 pm – 9:30 pm
Sat 11 am – 1 pm
Duration:
Number of lessons:
61 lessons + 3 exam
The course is intended for all those who have basic mathematical knowledge and want to deepen it and acquire new knowledge and skills related to machine learning.
After graduation, the student will master the minimum mathematical knowledge necessary to master machine learning. You will be able to apply mathematical methods in the context of solving certain problems (for example, data processing, forecasting and modeling). However, there is an important circumstance, in order to master all this, the student must not stop at least independently deepening the acquired mathematical knowledge.
After the course students will master operations with vectors and matrices, including eigenvalues, eigenvectors, and their application in data science, methods of dimensionality reduction (PCA), gradient descent computation in multidimensional spaces, probability theory distributions, distribution characteristics and properties, hypothesis testing, as well as optimization algorithms (RMSprop, Adam), advanced model design and application in machine learning (Logistic Regression, SVM, Decision Trees) and deep learning (Neural Networks, CNNs, RNNs, Transformers, Autoencoders, GANs).
before and after the course
For anyone who wants to gain practical and real-world knowledge about Machine Learning and become a Machine Learning Engineer, Data Scientist, or Data Analyst.
Developers, data analysts, and professionals with basic machine learning knowledge who want to deepen their knowledge in the field of machine learning.
The admission to the course is done through an interview and a test
Key topics teaching in advanced mathematics and probability theory
Python Language Learning in Machine Learning
Learning Used Libraries
Machine Learning Models
Introduction to eigenvalues and eigenvectors
Applications in data analysis and dimensionality reduction
Calculating derivatives
Further practical exercises in multivariate calculus for ML
Basic probability concepts
Regression and classification with decision trees
Stochastic gradient descent, batch gradient descent, mini-batch gradient descent, back propagation
Momentum, RMSprop, Adam
Introduction to Recurrent neural networks, LSTM, GRU
Attention, Transformer (Bert)
Autoencoders, Sparse Autoencoders, KL divergence, Variational Autoencoders
Introduction to GANs
Bayesian and Siamese networks
Compilation of a competent CV
Professional Linkedin Page Design
Preparation for the interview with the employer
Mentoring partner organizations in the relevant position
It is given in case of 30-49% progress
It is given in case of 50-79% progress
It is given in case of 80% + progress
Leave your details if you are from the BDG community
15 years of experience in the field
Professional trainers
Individual consultation
Assistance in finding a job
Courses for all levels
32,500+ graduates
Permanent discount for further lessons
450 successful students per year
Application of successful methods
An objective procedure for selecting students
3 types of certificates
Permanent contact with the student
Providing internship opportunities or job search assistance, from CV consultation to interview preparation
Individual approach in case of not understanding the topic, having a technical problem or any other issue
BDG community membership, joining 32,500+ BDG members and creating network with them.
Special conditions for participating in other online and offline courses and events
We are trusted by well-established, successful companies that prioritize education. We are certain that education will lead us to success․
Monthly
Certificate of completion
Lifetime Access
Disccord Communitty
Provided Resources
We are trusted by well-established, successful companies that prioritize education.
We are certain
that education will lead us to success․