Machine Learning for Beginners

ML engineer is a specialist who is at the intersection of data analysis and development. He must be able to write code, build mathematical models and understand business needs.

We have compiled the course program in such a way that anyone without a strong mathematical background can understand all stages of the work: from data collection to application classical algorithms before training neural networks and conducting A/B tests.

Looking for a job in a new profession is not easy, so we decided to share our experience and paid special attention to preparing for interviews and analyzing popular tasks .

In a word, in front of you in your hands is a comprehensive starter pack for starting a career in ML and Data Science.

COURSE PROGRAM

1. APPLICATION DEVELOPMENT IN PYTHON

Let's start with the basics of programming, learn how to write code in Python and master libraries for data analysis and machine learning. Let's learn how to work with databases and figure out how to obtain data for models using SQL queries. Let's talk about application architecture and learn how to control versions using Git. We will write a prototype of the future ML service and configure everything necessary for its operation.

2. MACHINE LEARNING

Let's get acquainted with classic machine learning algorithms. We'll look at everything from simple linear models to gradient boosting on decision trees. We will learn how to prepare data for models, configure various parameters and evaluate the quality of work of ML algorithms. We will discuss the intricacies of developing recommender systems, train the model on social network data and connect it with our application.

3. BASICS OF DEEP LEARNING

Deep learning and neural networks allow you to solve problems in which classical models are powerless: face recognition, detection of objects in images, generation of meaningful text. Let's look at popular neural network architectures, learn how to use pre-trained models and train our own. Let's build an advanced model and improve our recommendation algorithm.

4. STATISTICS AND A/B TESTS

Let's consider the basic concepts of probability theory and mathematical statistics. We will learn how to conduct A/B tests and reliably assess the impact of ML models on the product and business. We will discuss the pitfalls of conducting experiments and ways to evaluate metrics in situations where an A/B test is impossible to conduct. We will implement our testing system and find out whether we managed to improve the quality of recommendations in comparison with the basic solution.

5. PREPARATION FOR INTERVIEWS

We will share our experience and tell you how interviews for Junior ML engineer go: we will analyze algorithmic problems in Python, as well as popular questions on machine learning, statistics and A/B tests. Practical tasks will help you gain confidence in your knowledge, improve your skills in advance and confidently go through this difficult stage.