Machine Learning for Finance

Machine Learning for Finance, available at $49.99, has an average rating of 4.3, with 49 lectures, 7 quizzes, based on 47 reviews, and has 369 subscribers.

You will learn about How to tackle problems in Fintech and financial investments Learn feature engineering, EDA and understanding with regards to financial data Build an ANN-based model for predicting the stock prices Enhance your Machine Learning skills with ensemble models like random forest and XGBoost. Enhance your understanding of Neural Networks to build regression-based models. Learn how to identify fraudulent transactions by building a fraud detection model by using classification models. Achieve efficient frontier by using features like Sharpe ratios and risk management. This course is ideal for individuals who are This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning. It is particularly useful for This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning.

Enroll now: Machine Learning for Finance

Summary

Title: Machine Learning for Finance

Price: $49.99

Average Rating: 4.3

Number of Lectures: 49

Number of Quizzes: 7

Number of Published Lectures: 49

Number of Published Quizzes: 7

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

How to tackle problems in Fintech and financial investments

Learn feature engineering, EDA and understanding with regards to financial data

Build an ANN-based model for predicting the stock prices

Enhance your Machine Learning skills with ensemble models like random forest and XGBoost.

Enhance your understanding of Neural Networks to build regression-based models.

Learn how to identify fraudulent transactions by building a fraud detection model by using classification models.

Achieve efficient frontier by using features like Sharpe ratios and risk management.

Who Should Attend

This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning.

Target Audiences

This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning.

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.

About the Author

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.

Course Curriculum

Chapter 1: Financial Data Understanding, EDA, and Feature Engineering

Lecture 1: The Course Overview

Lecture 2: Visualization, EDA, and Feature Engineering of Financial Data

Lecture 3: Features of the Stock Data

Lecture 4: Univariate and Bivariate Analysis of Data

Lecture 5: Deriving Moving Average and RSI Based Features

Lecture 6: Data cleaning and Outlier Detection

Lecture 7: Creating the Features and Independent Variable

Lecture 8: Prepare Data for Modeling

Chapter 2: Predicting the FOREX Currencies by Building a Linear Model

Lecture 1: Linear Regression Intuition

Lecture 2: Understanding of FOREX Markets Data

Lecture 3: Pre-Process FOREX Currency Data for Model Input

Lecture 4: Building the Linear Regression Model

Lecture 5: R-Squared and Adjusted R-Squared as a Performance Metric

Lecture 6: The Testing Significance of Features by Using p-value and VIF

Lecture 7: Hyperparameter Tuning and Final Model Selection

Chapter 3: Tree-Based Machine Learning Techniques for Stock Prediction

Lecture 1: Decision Trees Intuition

Lecture 2: Entropy and Information Gain Criterion for Tree Construction

Lecture 3: Building a Decision Tree-Based Model for Predicting Stock Prices

Lecture 4: Train Using Different Max Depth

Lecture 5: Random Forest Intuition

Lecture 6: Build a Random Forest Regressor for Predicting Stock Prices

Lecture 7: Boosting and XGBoost Based Regression Model for Stock Prediction

Chapter 4: Artificial Neural Networks Basics and Intuition

Lecture 1: What a Neural Network Is

Lecture 2: Feed Forward in Neural Networks

Lecture 3: Gradient Descent in Neural Networks

Lecture 4: Back Propagation in Neural Networks

Lecture 5: Loss Function in Neural Networks

Lecture 6: Hyperparameters in Neural Networks

Chapter 5: Stock Price Prediction by Using Artificial Neural Networks

Lecture 1: Prepare Data for Ingestion into the Neural Network

Lecture 2: Define the Neural Network Layers and Model

Lecture 3: Visualize Keras Model by using Pydot

Lecture 4: Train the Model Using Basic Parameters

Lecture 5: Analyze the Model Performance Using Loss and Accuracy Curves

Lecture 6: Hyperparameter Tuning of Neural Network

Lecture 7: Generating Predictions by Using the Trained Model

Chapter 6: Modern Portfolio Theory and Techniques for Portfolio Management

Lecture 1: MPT and Stock Data Intuition

Lecture 2: Random Portfolio Generation and Portfolio Volatility

Lecture 3: Sharpe Ratio for Optimum Portfolio

Lecture 4: Portfolio Allocation Using Sharpe Ratio and Efficient Frontier

Lecture 5: Maximum Sharpe Ratio with SciPy Optimization

Lecture 6: Plotting and Visualizing Efficient Frontier

Lecture 7: Final Portfolio Allocation and Visualization

Chapter 7: Predicting Fraud in Financial Transactions by Using ANN classification

Lecture 1: Softmax and Sigmoid Activation in Neural Networks

Lecture 2: Categorical Cross Entropy Loss for Classification

Lecture 3: Feature Engineering and Preprocess Data for Input into the Model

Lecture 4: Creating the Model and the Optimizer

Lecture 5: Training the Model

Lecture 6: Handling Class Imbalance

Lecture 7: Evaluating the Final Model and Predict Fraud Using the Model

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 5 votes

2 stars: 3 votes

3 stars: 6 votes

4 stars: 11 votes

5 stars: 22 votes

Frequently Asked Questions

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