on-demand course

Machine Learning for Finance

with Aryan Singh

April 2020

Intermediate to advanced

4h 30m

English

Packt Publishing

Closed Captioning available in English

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Course outline

The Course Overview
6m 4s
Visualization, EDA and feature engineering of financial data
6m 40s
Features of the stock data
4m 32s
Univariate and Bivariate analysis of data
14m 29s
Deriving moving average and RSI based features
6m 3s
Data cleaning and outlier detection
6m 20s
Creating the features and independent variable
6m 37s
Prepare the data for modeling
3m 38s
Linear regression intuition
5m 41s
Understanding of FOREX markets data
4m 14s
Pre-process FOREX currency data for model input
10m 48s
Build the linear regression model
6m 55s
R-Squared and adjusted R-squared as a performance metric.
4m 19s
Testing significance of features by using p-value and VIF
8m 12s
Feature pruning and final model selection
8m 26s
Decision trees intuition
4m 55s
Entropy and information gain criterion for tree construction
7m 6s
Build a decision tree-based model for predicting stock prices
3m 6s
Training using different max depth.
4m 13s
Random forest intuition.
3m 22s
Build a Random Forest regressor for predicting stock prices
3m 54s
Boosting and XGBoost based regression model for stock prediction.
4m 34s
What is a neural network
8m 40s
Feed Forward in Neural Networks
4m 32s
Gradient Descent in Neural Networks
5m 42s
Backpropagation in Neural Networks
7m 53s
Loss Function in Neural Networks
4m 9s
Hyperparameters in Neural Networks
8m 0s
Prepare data for ingestion into the neural network.
3m 27s
Define the neural network layers and model.
2m 41s
Visualize Keras model by using Pydot
2m 52s
Train the model using basic parameters.
2m 12s
Analyze the model performance using loss and accuracy curves.
1m 34s
Hyperparameter tuning of neural network
8m 2s
Generating predictions by using the trained model
2m 47s
MPT and stock data intuition
8m 12s
Random portfolio generation and portfolio volatility
7m 20s
Sharpe ratio for optimum portfolio
3m 45s
Portfolio allocation using Sharpe ratio and efficient frontier
7m 20s
Maximize Sharpe ratio with SciPy Optimization
4m 57s
Plot and Visualize efficient frontier
8m 34s
Final Portfolio allocation and visualization
6m 25s
Softmax and sigmoid activation in neural networks.
2m 44s
Categorical cross entropy loss for classification
4m 1s
Feature engineering and preprocess data for input into the model
4m 56s
Create the model and the optimizer
2m 24s
Train the model
5m 7s
Handling Class Imbalance
2m 59s
Evaluate the final model and predict fraud using the model
5m 27s

Content preview from Machine Learning for Finance

Feature engineering and preprocess data for input into the model

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Publisher Resources

ISBN: 9781789535143Supplemental Content

Machine Learning for Finance

with Aryan Singh

Chapter 1 : Financial data understanding, EDA and feature Engineering

Chapter 2 : Predicting the FOREX currencies by building a linear model.

Chapter 3 : Tree-based Machine Learning Techniques For Stock Prediction

Chapter 4 : Artificial Neural Networks basics and Intuition

Chapter 5 : Stock price prediction by using Artificial Neural Networks

Chapter 6 : Modern Portfolio theory and techniques for Portfolio Management.

Chapter 7 : Predicting fraud in Financial Transactions by using ANN classification.

Content preview from Machine Learning for Finance

Feature engineering and preprocess data for input into the model

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

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Machine Learning for Finance

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Publisher Resources

Chapter 1 : Financial data understanding, EDA and feature Engineering

Chapter 2 : Predicting the FOREX currencies by building a linear model.

Chapter 3 : Tree-based Machine Learning Techniques For Stock Prediction

Chapter 4 : Artificial Neural Networks basics and Intuition

Chapter 5 : Stock price prediction by using Artificial Neural Networks

Chapter 6 : Modern Portfolio theory and techniques for Portfolio Management.

Chapter 7 : Predicting fraud in Financial Transactions by using ANN classification.

Content preview from Machine Learning for Finance

Feature engineering and preprocess data for input into the model

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

Machine Learning for Finance

Probabilistic Machine Learning for Finance and Investing

Deep Learning for Finance

Machine Learning and Data Science Blueprints for Finance

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.