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[Best Free Udemy Courses] Classification-Based Machine Learning for Finance


Classification-Based Machine Learning for Finance

Hands on guide on using classification based Machine Learning techniques with application in finance and investment

The 100% off coupons are never created by Udemy. They are created by the instructor that made the course. They tend to be used by the instructors to create the initial community and buzz for the course. So many courses, particularly the established and popular courses, won’t be available completely free. However, they do often feature in the Udemy Sitewide sales
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What Will I Learn?
  • Understand and implement logistic regression, linear discriminant analysis, quadratic discriminant analysis, Stochastic Gradient Descent classifier, Nearest Neighbors, Gaussian Naive Bayes and many more in Python for investment.
  • Design investment strategies following professional Quant work flow
  • Understanding and implement machine learning techniques utilizing Python scikit-learn library.
  • Gain a deeper appreciation of challenges of modelling financial time series
  • Understand the different categories of machine learning
  • Understand the limitations of various machine learning techniques
  • Develop a deep intuition of machine learning project and avoid making costly mistakes
  • Good understanding of Python data science stack.
  • Deep appreciation of finance, algorithmic trading, and hedge fund investment strategies
Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. 
In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.
In this course, we are first going to provide some background information to machine learning. To ease you into the machine learning lingo, we start will something that most people are familiar with – Logistic Regression. The assumptions of financial time series as well as the stylized facts are introduced and explained at length due to its importance. The assumptions of linear regression are also highlighted to demonstrate the challenges and danger of blindly applying machine learning to investment without proper care and considerations to the nuances of financial time series.
After covering the basics of classification based machine learning using logistic regression, we then move on to more advanced topics covering other classification machine learning algorithms such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, Stochastic Gradient Descent classifier, Nearest Neighbors, Gaussian Naive Bayes and many more. We follow the foundations that we started in the first regression based machine learning course covering cross-validation, model validation, back test, professional Quant work flow, and much more.
This course not only covers machine learning techniques, it also covers in depth the rationale of investing strategy development. 
This course is the second of the Machine Learning for Finance and Algorithmic Trading & Investing Series. The courses in the series includes:
  • Regression-Based Machine Learning for Algorithmic Trading
  • Classification-Based Machine Learning for Algorithmic Trading 
  • Ensemble Machine Learning for Algorithmic Trading 
  • Unsupervised Machine Learning: Hidden Markov for Algorithmic Trading 
  • Clustering and PCA for Investing
If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you. With over 30 machine learning techniques test cases, which included popular techniques such as Lasso regression, Ridge regression, SVM, XGBoost, random forest, Hidden Markov Model, common clustering techniques and many more, to get you started with applying Machine Learning to investing quickly. 
Who is the target audience?
  • Investment professionals who is interested to learn to apply classification-based machine learning techniques to investing strategies
  • Investment professionals and students who would are sourcing innovative investment ideas
  • Anyone looking for ideas and gentle introduction to how artificial intelligence (AI) can be applied to investing

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