- 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
Data scientists turn raw data into meaningful information that organizations can use to improve their businesses
Organizations are increasingly using and collecting larger amounts of data during their everyday operations. From predicting what people will buy to tackling plastic pollution, your job is to use data to find patterns and help solve the problems faced by businesses in innovative and imaginative ways.
You’ll extract, analyze and interpret large amounts of data from a range of sources, using algorithmic, data mining, artificial intelligence, machine learning, and statistical tools, in order to make it accessible to businesses. Once you’ve interpreted the data you’ll present your results using clear and engaging language.
Data scientists are in high demand across a number of sectors, as businesses require people with the right combination of technical, analytical, and communication skills.
Although we are in Ukraine, we also have students from different countries, as the training will be in Ukrainian, Russian, English, and even Spanish, and all training materials are in English!
TOP skills you will learn:
- Mathematical computing using popular Python packages as NumPy or Scikit-Learn
- How to prepare your data for model building (feature engineering)
- How to train and evaluate the performance of machine learning models
- How to tune the model’s hyperparameters and select models
- Understand and use linear/non-linear models
- Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, SVM, clustering and K-NN
- Get an understanding of how the magic of neural networks actually works and will be able to write them yourself
- Build reproducible machine learning pipelines
- Experience applying these methods to real-world problems
- Experience in building machine learning model APIs
- Lectures 47
- Quizzes 3
- Duration 24 week
- Skill level beginner
- Language English, Spanish, Ukrainian, Russian
- Students 0
- Assessments Yes
Math and Statistics
Machine learning is a technical science and, like any technical subject, uses a mathematical language to formulate ideas. A growing number of solutions are trying to automate the whole process of machine learning, but if a person does not understand the mathematical formalism underlying the algorithms, it is impossible to test and debug models that can lead to false conclusions. In this course, students learn the concepts of linear algebra, probability theory, and statistics that are key to exploratory data analysis, as well as understanding and developing machine learning algorithms.
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. This part of the program also describes data structures, relational and non-relational databases, means of interacting with databases, manipulating data, and merging datasets from different sources.
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 the types of problems they are designed to solve. Next, students get familiar with framing a machine learning problem, picking up appropriate objective functions and algorithms 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. This course also covers algorithms that are used when the target variable which has to be predicted is known. It starts with simple KNN and ends with fully connected feed-forward neural networks. 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.
- Formulating an ML problem
- Feature engineering
- Loss functions
- Generalization and performance estimation
- Hyperparameters optimization
- Model selection
- Linear regression
- Logistic regression
- k Nearest Neighbours
- Tree-based models
- Ensemble methods
- Support Vector Machine (SVM)
- Introduction to neural networks
- Recommendation systems
- Collaborative filtering
- Principal component analysis (PCA)
- k-means clustering
- Hierarchical clustering
- Anomaly detection
- Data Science project workflow
- Model deployment
- Time series analysis
- Final test