Fundamentals of Machine Learning in Finance

Colleagues, the Fundamentals of Machine Learning in Finance program will equip you in 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 a 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. Training modules equip you in 1) Fundamentals of Supervised Learning in Finance - What is Machine Learning in Finance?, Introduction to Fundamentals of Machine Learning in Finance, Support Vector Machines, SVM. The Kernel Trick, Example: SVM for Prediction of Credit Spreads, Tree Methods. CART Trees-Random Forests-Boosting, 2) Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction - Core Concepts of UL, PCA for Stock Returns, Dimension Reduction with PCA-tSNE-Autocoder, 3) Data Visualization & Clustering - Clustering Algorithms,  K-clustering, K-means Neural Algorithm, Hierarchical Clustering Algorithms, Clustering and Estimation of Equity Correlation Matrix, Minimum Spanning Trees, Kruskal Algorithm, Probabilistic Clustering, Sequence Modeling and Reinforcement Learning, and 4) Latent Variables, Sequence Modeling, Latent Variables for Sequences, State-Space Models, Hidden Markov Models, Neural Architecture for Sequential Data, Reinforcement Learning, Markov Decision Process and RL, Bellman Equation and Inverse Reinforcement Learning.

Enroll today (individuals & teams welcome): https://tinyurl.com/6unjfpm2 

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