Become TOP specialist within 15 weeks and join top data-driven companies. This is the right place for you if you have a desire for an impactful work. Get knowledge that can change the future of humanity!
No age or background limits. You don’t have to be a computer scientist to launch your career in Data Science.
Bootcamp is an intensive 15 weeks program which equals 2 years of university course. Perceive information quickly and get results in a short amount of time.
Free pre-course workshop
Some high school mathematics level is required, so we offer free pre-course trainings to pull up your knowledge
We guarantee 100% employment. If you don’t get a job we will refund you the money*
*You are required to participate in at least 95% of class time and completed 100% of the homework and score between 90-100 points
In Tbilisi campus we offer Data Science course designed with the needs of the employer. You will be taught knowledge through practice and you’ll learn how to learn.
Transform your career with the most in-demand data science bootcamp. This is an intensive course, where you can learn the advanced topics of Data Science, Machine Learning and AI. You will get acquainted with technology trends and learn up-to-date information from well known Data Science guru.
TOP skill you will learn:
- Experience using computer languages (Python, SQL, etc.) to manipulate data and draw insights from large data sets.
- Deep knowledge of fundamentals of machine learning
- Data mining, and statistical predictive modeling
- Experience applying these methods to real-world problems.
- Experience of driving and delivering analytics models and solutions
This is exactly for you if you are:
- a person looking for a career change
- a graduate from universities looking for a job in Data Science
- a developer with a mathematical mindset who would like to get career growth
- a business owner who would like to utilize data analysis and implement data-driven and AI projects
- a Data Scientist practitioner who wants to systematize the knowledge and to master Deep Learning
This is exactly for you if you are:
a person looking for a career change
a graduate from universities looking for a job in Data Science
a developer with a mathematical mindset who would like to get career growth
a business owner who would like to utilize data analysis and implement data-driven and AI projects
a Data Scientist practitioner who wants to systematize the knowledge and to master Deep Learning
max 30 students
675 hours / 15 weeks
Module 1: Python
Python is data scientists’ preferred programming language. If machine learning researchers decide to open source their work they will most likely do it in python. Therefore, the course starts by introducing python concepts and packages that are useful for data analysis.
- Booleans and Conditionals
- Reading and Writing Files
- Matlotlib/Seaborn for Data Visualization
Module 2: Data Engineering
“More data beats better algorithms” – this quote by Peter Norvig emphasizes great importance of data in machine learning. This part of the program describes data structures, relational and non-relational databases, means of interacting with databases, manipulating data and merging datasets from different sources.
- Data structures
- Relational Databases
- NoSQL databases
- Accessing Data Through APIs
- Web Scraping
Module 3: Math for Machine Learning
Machine learning is a technical science and like any technical subject it uses mathematical language to formulate ideas. There is an increasing number of solutions that try to automate the whole machine learning process but if one does not understand mathematical formalism behind algorithms it is impossible to test and debug models which can lead to spurious insights.
In this course students learn those concepts of linear algebra, probability theory and statistics that are essential for exploratory data analysis, understanding and designing machine learning algorithms.
- Linear Algebra
- Differential Calculus
- Probability Theory
- Bayes Theorem
- Statistical Quantities
Module 4: Intro to Machine Learning and Pre-Training Phase
This module starts with an introduction to machine learning: how it is organized, what are the sub branches of machine learning, fundamental differences between these approaches and types of problems they are designed to solve.
Next, students get familiar with framing a machine learning problem, picking up appropriate objective function and algorithm according to a given problem. It is well known that data wrangling and feature engineering takes most of the time of model development. Students learn techniques to effectively deal with missing values, outliers, categorical variables and design new features.
- Formulating an ML problem
- Data Preparation
- Feature engineering
Module 5: Supervised Learning - Training Phase
This course covers algorithms that are used when target variable which has to be predicted is known. It starts with simple KNN and ends with fully connected feed forward neural networks.
- Lazy Learner – K Nearest Neighbo8urs
- Linear Regression
- Loss Function
- Optimizing Loss Function
- Regularization: L1 and L2
- Data Partitioning
- Logistic Regression
- Multiclass Classifier: Softmax Regression
- Classification and Regression Trees
- Boosting: Adaboost, Gradient Boosting Machine
- Model Ensembling
- Intro to Bayesian Learning
- Intro to Neural Networks
- Training Neural Networks
Module 6: Supervised Learning - Testing Phase
Proper testing of a model is essential to build a reliable product. Students are introduced to various testing methods and parameters that help to build generalizable and stable models.
- Model Performance Indicators
- Learning Curve
- Loss Curve
- Bias and Overfit
Module 7: Unsupervised Learning
Most of the time values of the target variable are unknown and that is when unsupervised learning techniques are needed. They enable us to identify hidden structures in multidimensional datasets.
- Dimensionality Reduction
- Clustering: K-Means
- Anomaly Detection
Module 8: Optimization Techniques
Machine learning algorithms depend on parameters that are not optimized by the algorithm itself. Such parameters are called hyperparameters. Usually advanced machine learning algorithms depend on many hyperparameters and thus finding the optimal values of them is complex and time consuming task. The last part of the program covers techniques that automate hyperparameter optimization process.
- Latin Hypercube Sampling
- Grid Search and Random Search
- Thompson Sampling
- Multi Armed Bandit
To test and assess students’ knowledge each of them picks a machine learning problem after completing all modules and tries to find a solution by going through the data preparation, model training and testing phases.
Our trainers are qualified experts with at least 5 years of experience in IT, extensive real-world knowledge and coaching skills. They are knowledgeable practitioners, who want to give you the most value and practical knowledge. Our tutors are ready to reply to essentially all of your queries.
The price for full course
3000$ / per course
15 weeks of immersive learning
300 hours of lectures
500 hours of practical experience
new career within 3 months
fresh start into prosperous future