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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 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. Curriculum 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; Adaboost; XGBoost; Support Vector Machine (SVM); Introduction to neural networks; Recommendation systems; Collaborative filtering. TOP skill you will learn: Mathematical computing using popular Python packages as NumPy or Scikit-Learn How to use linear/non-linear models 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 Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, SVM, clustering and K-NN Get understanding about 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 of building machine learning model APIs This is exactly for you if you are: a person who already knows Python, SQL, linear algebra, calculus and statistics 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

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. Curriculum Linear algebra; Differential calculus; Probability theory; Bayes theorem; Distributions of random variables; Null hypothesis significance testing; Outliers; Exploratory data analysis. TOP skill you will learn: Foundations of linear algebra, calculus, probability theory, and statistics; How to read complex mathematical equations that underlie all machine learning algorithms; How to understand the mathematics behind machine learning algorithms; How to think abstractly. 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

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. Curriculum Variables and data structures; Conditional statements; Loops (for, while); Functions and methods; Object-Oriented Programming (OOP); Packages NumPy, SymPy, Pandas; Data visualization: Matplotlib, seaborn, plot.ly; Git/GitHub; Coding style guidelines; Reading and writing files; Relational databases; SQL queries; Workbench; Internet data (API, HTTP requests); Data cleaning. TOP skill you will learn: The basics of Python programming language; How to use Python packages for data mining; How to manipulate data and draw insights from large data sets; How to create clear and human-readable data visualization. How to get data from different sources (files, databases, API requests); How to write complex SQL queries; How to manipulate data and draw insights from large data sets; How to clean the data and perform exploratory data analysis (EDA). 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

Probability and statistics are essential for people who work with Data, especially in Data Analysis and or Data Science. During this course you will be introduced to the basics, such as what is a random variable, probability of its occurrence and probability mass/density function. Next, more complex topics. You will be able to perform exploratory data analysis to draw insights from the raw data. In addition to this, you will understand the math behind A/B testing using statistical hypothesis testing framework, that will help with decision making. Curriculum Intro to Probabilistic Thinking; Distributions of Random Variables; Main Characteristics of Distribution; Quantities of Information; Outliers in the Data; Approximation Results and Confidence Intervals; Significance Testing, Part 1: Inference for a Mean, Inference for a Proportion; Significance Testing, Part 2: Power of the Test, Effect Size; Extra Day for Q&A and More Practice. You will learn: What are the types of random variables and how they are distributed; How to use Bayes’s rule to find the probability of event occurrence; How to calculate main characteristics of a distribution, such as mean, median and variance; How to detect outliers in the data and how to deal with them; What are confidence intervals and why do we need them; What is a p-value and effect size of the test; How to perform a null hypothesis significance testing. This is exactly for you if: You are looking for a career change into Data Science/Analysis; You need to boost your stats knowledge and skills; You want to know how to run A/B tests; You are already working in a Data Science field and want to systematize your knowledge.