DS204BKK
Python for ML
Faculty
Valery Marchenkov
Data Scientist at S7 Airlines. Visiting Lecturer at MISIS.
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This comprehensive course shows how the Python programming language and its libraries are used to implement machine learning and data mining systems. In three parts, the main modern tools for data analysis, machine learning, and neural networks are considered, starting from the basics of language structure and data manipulation to the basics of machine learning and neural networks models.
Learning highlights
- Learn how Python is used in Machine Learning applications
- Apply modern programming tools to Data Science and Machine Learning problems
- Neural network models in practice
Course outline
15 classes
Session 1
Python Basics and Jupyter Notebooks
Session 2
Object-Oriented Programming in Python
Session 3
Matrix Algebra with Python and NumPy
Session 4
Tabular Data Processing with Pandas
Session 5
Exploratory Data Analysis (EDA). Data Cleansing
Session 6
Data Visualization
Session 7
Machine Learning with scikit-learn, p.1 Data Processing
Session 8
Machine Learning with scikit-learn, p.2 Algorithms
Session 9
Image Processing and Computer Vision
Session 10
Text Data and Natural Language Processing
Session 11
Tensor Algebra with PyTorch
Session 12
Deep Learning with PyTorch, p.1 Computer Vision
Session 13
Deep Learning with PyTorch, p.2 Natural Language Processing
Session 14
Deep Learning Models Fine-tuning with PyTorch
Session 15
Neural Networks Applications
Prerequisites
Basics of: Python syntax, Linear Algebra, Calculus, Statistics and Probability.
Knowledge of specific Math for ML topics and ML/DL are recommended, but not strictly required.
Methodology
The course is made up of 15 three-hour practical workshops and live coding sessions. Some required material is discussed in lecture format before practice.
The course can be divided into three main blocks:
Data Manipulation (Python, NumPy, SciPy, Pandas, Matplotlib)
Traditional Machine Learning Basics (Scikit-Learn, NLTK, OpenCV etc.)
Deep Learning and Neural Networks basics with PyTorch
Homeworks are designed as practical ML problems with graded demonstrations, one per week.
Grading
Valery is a Data Scientist at S7 Airlines. He works on aircraft engines and the fleet's recorded data in terms of fuel efficiency and maintenance planning algorithms development, travelers purchase, flight data and recommender systems. He also works as a Practice Instructor for Machine Learning courses at the Moscow Institute of Physics and Technology (MIPT) and as a Deep Learning lecturer at the NUST MISIS.
Before that he worked as a Structural Analysis Engineer at Boeing, where he worked on airframe design, static strength and fatigue analysis of metal parts for prospective aircrafts.
See full profileApply for this course
Python for ML
by Valery Marchenkov
Total hours
45 Hours
Dates
Oct 23 - Nov 10, 2023
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.