Data Scientist Career Track

Track

Certificate

Основи Machine Learning

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Трек знайомить з основними поняттями та методами в галузі Machine Learning (Машинного Навчання). Цей комплексний навчальний трек охоплює різні важливі теми, включаючи використання бібліотеки scikit-learn для запуску машинного навчання, застосування лінійної регресії для прогнозного моделювання, вивчення методів класифікації для категоризації даних, а також вивчення алгоритмів кластеризації для виявлення закономірностей у наборах даних. Show more

python

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What you will learn

Gain foundational insights into Machine Learning concepts.
Explore data types and preprocessing techniques in Scikit-learn.
Understand Machine Learning workflows including training sets and models.
Grasp data scaling methods: StandardScaler, MinMaxScaler, MaxAbsScaler.
Discover the structure and implementation of pipelines and ColumnTransformer.
Develop skills in evaluating models using cross-validation, GridSearchCV, and RandomizedSearchCV.
Assemble a complete Machine Learning pipeline integrating preprocessing and modeling.

145 chapters
16 hours
120 tasks

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Learning track content

Module 1 / ML Introduction with scikit-learn

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Module 2 / Linear Regression with Python

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Module 3 / Classification with Python

Module 4 / Identifying Spam Emails

In this project, we are going to classify spam email according to their content.

Module 5 / Cluster Analysis

First of all, let's understand what is clustering, why we need it, and consider some unique concepts about clustering problems. We will also look at the classification of clustering algorithms according to the principle of their work.

Now let's look at the most popular clustering algorithms in more detail: we will understand the principles of their work, look at their implementation in Python, and have some practice in solving simple tasks using these algorithms.

Finally, we will understand how to choose the best model and solve real tasks based on evaluation metrics and other validation techniques.

Module 6 / Clustering Demystified

In this project, we are going to understand what a cluster is and how to use it in Python.

Module 7 / Ensemble Learning

What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.

Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.

Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

Module 8 / Logistic Regression Mastering

In this project, we are going to understand Logistic Regression.

Requirements

A computer with a browser - all browsers are supported.
Your enthusiasm to enhance your tech skills.
Everything else needed to start learning and practicing is already included in this course.

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