Coursera – Machine Learning and Reinforcement Learning in Finance Specialization 2023-12 – Downloadly

Description

Machine Learning and Reinforcement Learning in Finance Specialization, The main goal of this specialization is to provide the knowledge and practical skills required to develop a strong foundation on the core paradigms and algorithms of Machine Learning (ML) with a special focus on the applications of ML to various practical problems in finance. The specialisation aims to enable students to solve practical ML-solvable problems they may face in real life including: 1. mapping the problem onto the general landscape of available ML methods, 2. Choosing the particular ML approach that will be best suited to solve the problem, and 3. Successfully implementing a solution, and assessing its performance.

Specialization The specialization is essentially in ML where all examples, home assignments and course projects deal with various problems in finance (e.g. stock trading, asset management and banking applications), and the choice of topics is driven by a focus on ML methods that are used by practitioners in finance. The specialization aims to prepare students to work on complex machine learning projects in finance, which often require both a broad understanding of the entire field of ML and an understanding of the suitability of various methods available in a particular subfield of ML (e.g., unsupervised learning) for solving practical problems they encounter in their work.

What you will learn

Compare ML for Finance to ML in Technology (Image and Speech Recognition, Robotics, etc.)
Describe linear regression and classification models and methods for their evaluation
Explain how reinforcement learning is used for stock trading
Get acquainted with popular methods of modeling market frictions and feedback effects for options trading.

Who is this course for

PRespondents Working in financial institutions, such as banks, asset management firms, or hedge funds
Individuals Interested in applications of ML for individual day trading
current Full-time students pursuing degrees in Finance, Statistics, Computer Science, Mathematics, Physics,