Machine Learning for Finance

Focused View

Aryan Singh

4:30:24

10 View

1.Financial Data Understanding, EDA, and Feature Engineering

01.The Course Overview.mp4

06:04

02.Visualization, EDA, and Feature Engineering of Financial Data.mp4

06:40

03.Features of the Stock Data.mp4

04:32

04.Univariate and Bivariate Analysis of Data.mp4

14:28

05.Deriving Moving Average and RSI Based Features.mp4

06:03

06.Data cleaning and Outlier Detection.mp4

06:19

07.Creating the Features and Independent Variable.mp4

06:37

08.Prepare Data for Modeling.mp4

03:37

2.Predicting the FOREX Currencies by Building a Linear Model

09.Linear Regression Intuition.mp4

05:41

10.Understanding of FOREX Markets Data.mp4

04:13

11.Pre-Process FOREX Currency Data for Model Input.mp4

10:48

12.Building the Linear Regression Model.mp4

06:54

13.R-Squared and Adjusted R-Squared as a Performance Metric.mp4

04:18

14.The Testing Significance of Features by Using p-value and VIF.mp4

08:12

15.Hyperparameter Tuning and Final Model Selection.mp4

08:25

3.Tree-Based Machine Learning Techniques for Stock Prediction

16.Decision Trees Intuition.mp4

04:54

17.Entropy and Information Gain Criterion for Tree Construction.mp4

07:05

18.Building a Decision Tree-Based Model for Predicting Stock Prices.mp4

03:06

19.Train Using Different Max Depth.mp4

04:13

20.Random Forest Intuition.mp4

03:22

21.Build a Random Forest Regressor for Predicting Stock Prices.mp4

03:53

22.Boosting and XGBoost Based Regression Model for Stock Prediction.mp4

04:33

4.Artificial Neural Networks Basics and Intuition

23.What a Neural Network Is.mp4

08:39

24.Feed Forward in Neural Networks.mp4

04:31

25.Gradient Descent in Neural Networks.mp4

05:41

26.Back Propagation in Neural Networks.mp4

07:52

27.Loss Function in Neural Networks.mp4

04:08

28.Hyperparameters in Neural Networks.mp4

07:59

5.Stock Price Prediction by Using Artificial Neural Networks

29.Prepare Data for Ingestion into the Neural Network.mp4

03:26

30.Define the Neural Network Layers and Model.mp4

02:40

31.Visualize Keras Model by using Pydot.mp4

02:52

32.Train the Model Using Basic Parameters.mp4

02:12

33.Analyze the Model Performance Using Loss and Accuracy Curves.mp4

01:33

34.Hyperparameter Tuning of Neural Network.mp4

08:02

35.Generating Predictions by Using the Trained Model.mp4

02:47

6.Modern Portfolio Theory and Techniques for Portfolio Management

36.MPT and Stock Data Intuition.mp4

08:12

37.Random Portfolio Generation and Portfolio Volatility.mp4

07:19

38.Sharpe Ratio for Optimum Portfolio.mp4

03:44

39.Portfolio Allocation Using Sharpe Ratio and Efficient Frontier.mp4

07:20

40.Maximum Sharpe Ratio with SciPy Optimization.mp4

04:56

41.Plotting and Visualizing Efficient Frontier.mp4

08:33

42.Final Portfolio Allocation and Visualization.mp4

06:25

7.Predicting Fraud in Financial Transactions by Using ANN classification

43.Softmax and Sigmoid Activation in Neural Networks.mp4

02:44

44.Categorical Cross Entropy Loss for Classification.mp4

04:00

45.Feature Engineering and Preprocess Data for Input into the Model.mp4

04:56

46.Creating the Model and the Optimizer.mp4

02:24

47.Training the Model.mp4

05:06

48.Handling Class Imbalance.mp4

02:59

49.Evaluating the Final Model and Predict Fraud Using the Model.mp4

05:27

Description

Machine Learning for Finance is a perfect course for financial professionals entering the fintech domain. It shows how to solve some of the most common and pressing issues facing institutions in the financial industry, from retail banks to hedge funds. This video course focuses on Machine Learning and covers a range of analysis tools, such as NumPy, Matplotlib, and Pandas. It is packed full of hands-on code simulating many of the problems and providing working solutions. This course aims to build your confidence and the experience to go ahead and tackle real-life problems in financial analysis. The industry is adopting automatic, data-driven algorithms at a rapid pace, and Machine Learning for Finance gives you the skills you need to be at the forefront. By the end of this course, you will be equipped with all the tools from the world of Finance, machine learning and deep learning essential for tackling all these pressing issues in the area of Fintech. The code files to this videos is also available on This GitHub repo: https://github.com/PacktPublishing/Machine-Learning-for-Finance-video

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Machine Learning

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Aryan Singh

Instructor's Courses

Aryan Singh is a data scientist with a penchant for solving business problems across different domains by using machine learning and deep learning. He is an avid reader and has a keen interest in NLP research. He loves to participate and organize hackathons and has won a number of them. Currently, he works as a data scientist at Publicis Sapient. https://www.linkedin.com/in/aryansingh1/

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.