Fundamentals of Machine Learning in Finance

Colleagues, the Fundamentals of Machine Learning in Finance program equips you to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more in-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course Skill-based training modules address: 1) Fundamentals of Supervised Learning in Finance, 2) Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction, 3) 3) Data Visualization & Clustering, and 4) Sequence Modeling and Reinforcement Learning.

Enroll today (individuals & teams welcome): https://tinyurl.com/424wvad3 

Much career success, Lawrence E. Wilson - Financial Certification Academy