Classification-Based Machine Learning for Finance – Anthony Ng

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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.

Purchase Classification-Based Machine Learning for Finance – Anthony Ng courses at here with PRICE $98 $42

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Classification-Based Machine Learning for Finance

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

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.

Course Curriculum

Introduction

• Introduction (3:07)
• Feedback (4:26)
• Obtaining the Course Resources (3:28)
• How to Succeed In This Course (6:03)

Introduction To Machine Learning For Algorithmic Trading

• Brief Introduction to Machine Learning (5:38)
• Machine Learning Project Check List Part 1 (12:43)
• Machine Learning Project Check List Part 2 (10:00)
• Model Selection and Quant Workflow (8:24)
• Financial Time Series Characteristics (5:58)

Logistic Regression

• Understanding Logistic Regression (11:16)
• Logistic Regression and Scikit Learn (13:55)

Classification – A Walk Through Tutorial

• Understanding Classification ML and Data Exploration (9:12)
• Building a Simple Classifier and Performing Cross Validation (15:23)
• Confusion Matrix (15:51)
• Precision/Recall Tradeoff (14:25)
• The Receiver Operating Characteristics (ROC) Curve (8:49)

Default Prediction

• Template (10:27)
• Default prediction with LDA, KNN and Random Forest (12:10)

Predicting Next Day’s Returns

• Background to Returns Prediction (9:14)
• Predicting Next Day’s Returns Using Logistic Regression (10:01)
• Predicting Next Day’s Returns Using LDA and QDA (7:33)
• Price Prediction Using Real Market Data from Quantopian (7:10)
• Back Test and Tear Sheet (16:03)

Ideas

• Ideas (18:29)

Global Stock Selection Strategy

• Introduction to Alpha Factors (8:32)
• Global Stock Selection Strategy (7:13)

Bonus Section

• Bonus Lecture (1:46)

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