Machine Learning In Finance (2024)

MACHINE LEARNING IN FINANCE

Master the most in-demand skill-set of the world's top financial institutions with one of the most practical, comprehensive and affordable courses in Financial Machine Learning.

Enroll : $250

Watch Intro Video

15+ Real-World Practical Applications

Case studies along with their python-based implementation.

Financial Applications Coverage

- Algo Trading
- Portfolio Management
- Fraud detection
- Leanding and Loand Default prediction
- Sentiment Analysis
- Derivatives Pricing and Hedging
- Asset Price Prediction
- and many more

Machine Learning concepts customized to Finance

Separate modules for each AI and Machine Learning Type with exhausive concepts.

Supervised Learning
Regression and Classification mdoels
1. Linear and Logistic Regression
2. Random Forest and GBM
3. Deep Neural Network (including RNN and LSTM)
Includes 6+ case studies
Unsupervised Learning
Clustering and Dimensionality Reduction
1. Principal Component Analysis
2. k-Means and hierarchical clustering
Includes 5+ case studies
Reinforcement Learning and NLP
Value/Policy based RL models and sentiment analysis
1. Deep Q- Learning RL model
2. Policy based RL models
3. Sentiment based trading
Includes 4+ case studies

Python Tool, Code And Access To Free Data

Include exhaustive coverage of python packages from data wrangling to deep learning along with access to historical data of 100,000+ instruments.

Python Tools. and Packages
Data Wrangling, Deep Learning and Backtesting
1. Keras and Tensorflow - Machine Learning/Deep Learning
2. Data Wrangling - Pandas, Numpy
3. Visualization - Matplotlib, seaborn
4. Backtesting - Backtrader
Free financial data - access and usage
Instrument data, macroeconomic data, fundamentals and alternative data
1. Yahoo Finance/Quandl - 50+ exchanges
2. FRED -Macroeconomic data
3. Kaggle
4. Custom data and many more

11 modules

Each module containing several videos with 5+ hours content with several quizzes.
10000+ lines of code

Plug and play machine learning master templates and code for 15+ case studies
Course Certificate

Certificate shareable on social media and in resumes.
Webinars

Attend several webinars and session related to course and latest updates on Financial Machine Learning
Community

Be a part of vibrant peer group
Affordable

Much lower valuation compared to expensive $1000+ courses.

Course Curriculum

1. Welcome
2. Course Structure
3. Getting the most out of it
4. Course Code
1. 2.1 Application of ML in Finance - Introduction
2. 2.2 Application of ML in Finance - Applications FREE PREVIEW
3. 2.3 Types of Machine Learning
4. Quiz FREE PREVIEW
1. 3.1 Why Python
2. 3.2 Packages and Installation needed for the course
3. Quiz
4. 3.3.1 Machine Learning Modelling Steps - Problem Definition
5. 3.3.2 Machine Learning Modelling Steps - Loading the data
6. 3.3.3 Machine Learning Modelling Steps - Data analysis
7. 3.3.4 Machine Learning Modelling Steps - Data preparation
8. 3.3.5 Machine Learning Modelling Steps - Evaluate models
9. 3.3.6 Machine Learning Modelling Steps - Model tuning
10. 3.3.7 Machine Learning Modelling Steps - Finalize the model
1. 4.1 Architecture
2. 4.2 Training
3. 4.3 Hyperparameters
4. Quiz
5. 4.4.1 Creating an ANN in Python - Processing the Dataset
6. 4.4.2 Creating an ANN in Python - Building the ANN
7. 4.4.3 Creating an ANN in Python - ANN Training and Evaluation
1. 5.1 How is Supervised Learning used in Finance?
2. 5.2 Prerequisites/Additional Study Material
3. 5.3 Types of Supervised Learning
4. 5.4 Linear Regression
5. 5.5 Regularized Regression
6. 5.6 Logistic Regression
7. 5.7 Support Vector Machines
8. 5.8 K-Nearest Neighbors
9. 5.9 Linear Discriminant Analysis
10. 5.10 Classification and Regression Trees
11. 5.11 Introduction to Ensemble Methods
12. 5.11.1 Random Forest
13. 5.11.2 Extra Trees
14. 5.11.3 Adaptive Boosting
15. 5.11.4 Gradient Boosting Method
16. 5.12 Artificial Neural Networks
17. 5.13 Model Selection
18. 5.14 Model Performance
19. Quiz
1. 6.1 Use cases in Finance
2. 6.2 Relationship with time series models
3. 6.3.1 Overview of time series model - components of a time series
4. 6.3.2 Overview of time series model - autocorrelation and stationarity
5. 6.3.3 Overview of time series model - traditional times series models
  See Also
  Requirements – Interdisciplinary Studies Field Center for Consciousness Center . Tucson . Arizona
6. 6.4 Converting time series models to supervised learning models
7. 6.5.1 Regression and Time Series Master Template - Introduction
8. 6.5.2 Regression and Time Series Master Template - Getting Started
9. 6.5.3 Regression and Time Series Master Template - Data Analysis
10. 6.5.4 Regression and Time Series Master Template - Data Preparation
11. 6.5.5 Regression and Time Series Master Template - Algorithms and Models
12. 6.5.6 Regression and Time Series Master Template - Model Tuning
13. 6.5.7 Regression and Time Series Master Template - Finalize Model
14. 6.6.1 Using Deep Learning models for Time series - Overview
15. 6.6.2 Using Deep Learning models for Time series - RNN and LSTM
16. Quiz
17. 6.7.1 Case Study 1 - Predicting Stock Price - Background FREE PREVIEW
18. 6.7.2 Case Study 1 - Predicting Stock Price - Getting Started
19. 6.7.3 Case Study 1 - Predicting Stock Price - Data Analysis
20. 6.7.4 Case Study 1 - Predicting Stock Price - Data Preparation
21. 6.7.5 Case Study 1 - Predicting Stock Price - Algorithms and Models
22. 6.7.6 Case Study 1 - Predicting Stock Price - Model Tuning
23. 6.7.7 Case Study 1 - Predicting Stock Price - Finalize Model
24. 6.7.8 Case Study 1 - Download Code and Data
25. Quiz
26. 6.8.1 Case Study 2 - Pricing a Derivative - Background
27. 6.8.2 Case Study 2 - Pricing a Derivative - Getting Started
28. 6.8.3 Case Study 2 - Pricing a Derivative - Data Analysis
29. 6.8.4 Case Study 2 - Pricing a Derivative - Data Preparation
30. 6.8.5 Case Study 2 - Pricing a Derivative - Algorithms and Models
31. 6.8.6 Case Study 2 - Pricing a Derivative - Model Tuning
32. 6.8.7 Case Study 2 - Pricing a Derivative - Finalize Model
33. 6.8.8 Case Study 2 - Download Code and Data
34. Quiz
35. 6.9.1 Case Study 3 - Investor Risk Tolerance - Background
36. 6.9.2 Case Study 3 - Investor Risk Tolerance - Getting Started
37. 6.9.3 Case Study 3 - Investor Risk Tolerance - Data Preparation
38. 6.9.4 Case Study 3 - Investor Risk Tolerance - Feature Selection
39. 6.9.5 Case Study 3 - Investor Risk Tolerance - Algos and Models
40. 6.9.6 Case Study 3 - Investor Risk Tolerance - Model Tuning
41. 6.9.7 Case Study 3 - Investor Risk Tolerance - Finalize Model
42. 6.9.8 Case Study 3 - Download Code and Data
43. Quiz
44. ModuleAssignment
1. 7.1 Use cases in Finance
2. 7.2 Focus of this module
3. 7.3.1 Classification Master Template - Introduction
4. 7.3.2 Classification Master Template - Getting Started
5. 7.3.3 Classification Master Template - Data Analysis
6. 7.3.4 Classification Master Template - Data Preparation
7. 7.3.5 Classification Master Template - Algorithms and Models
8. 7.3.6 Classification Master Template - Model Tuning
9. 7.3.7 Classification Master Template - Finalize Model
10. 7.4.1 Case Study 1 - Fraud Detection - Background
11. 7.4.2 Case Study 1 - Fraud Detection - Getting Started
12. 7.4.3 Case Study 1 - Fraud Detection - Data Preparation
13. 7.4.4 Case Study 1 - Fraud Detection - Algorithms and Models
14. 7.4.5 Case Study 1 - Fraud Detection - Model Tuning
15. 7.4.6 Case Study 1 - Fraud Detection - Finalize Model
16. 7.4.7 CaseStudy1 - Download Code and Data
17. 7.5.0 Quiz
18. 7.5.1 Case Study 2 - Loan Default Probability - Background
19. 7.5.2 Case Study 2 - Loan Default Probability - Getting Started
20. 7.5.3 Case Study 2 - Loan Default Probability - Data Preparation
21. 7.5.4 Case Study 2 - Loan Default Probability - Feature Engineering
22. 7.5.5 Case Study 2 - Loan Default Probability - Algorithms and Models
23. 7.5.6 Case Study 2 - Loan Default Probability - Model Tuning
24. 7.5.7 Case Study 2 - Loan Default Probability - Finalize Model
25. 7.5.8 CaseStudy2 - Download Code and Data
26. 7.6.0 Quiz
27. 7.6.1 Case Study 3 - Bitcoin Trading Strategy - Background
28. 7.6.2 Case Study 3 - Bitcoin Trading Strategy - Getting Started
29. 7.6.3 Case Study 3 - Bitcoin Trading Strategy - Data Preparation
30. 7.6.4 Case Study 3 - Bitcoin Trading Strategy - Algorithms and Models
31. 7.6.5 Case Study 3 - Bitcoin Trading Strategy - Model Tuning
32. 7.6.6 Case Study 3 - Bitcoin Trading Strategy - Backtesting Model
33. 7.6.7 CaseStudy3 - Download Code and Data
34. Quiz
35. 7.7 ModuleAssignment
1. 8.1 Use cases in Finance
2. 8.2 Focus of this module
3. 8.3.1 Theory and concepts - PCA
4. 8.3.2 Theory and concepts - SVD
5. 8.3.3 Theory and concepts - kPCA
6. 8.3.4 Theory and concepts - tSNE
7. 8.4.1 Dimensionality Reduction Master Template - Intro
8. 8.4.2 Dimensionality Reduction Template - Data Prep
9. 8.4.3 Dimensionality Reduction Master Template - PCA
10. 8.4.4 Dimensionality Reduction Master Template - SVD
11. 8.4.5 Dimensionality Reduction Master Template - KPCA
12. 8.4.6 Dimensionality Reduction Master Template - t-SNE
13. 8.5.0 Quiz
14. 8.5.1 Case Study 1 - Portfolio Management - Background
15. 8.5.2 Case Study 1 - Portfolio Management - Getting Started
16. 8.5.3 Case Study 1 - Portfolio Management - Data Prep
17. 8.5.4 Case Study 1 - Portfolio Management - Models
18. 8.5.5 Case Study 1 - Portfolio Management - Portfolio Sel
19. 8.5.6 Case Study 1 - Portfolio Management - Backtesting
20. 8.5.7 CaseStudy1 - Download Code and Data
21. 8.6.0 Quiz
22. 8.6.1 Case Study 2 - Yield Curve - Background
23. 8.6.2 Case Study 2 - Yield Curve - Getting Started
24. 8.6.3 Case Study 2 - Yield Curve - Data Analysis
25. 8.6.4 Case Study 2 - Yield Curve - Data Preparation
26. 8.6.5 Case Study 2 - Yield Curve - PCA
27. 8.6.6 Case Study 2 - Yield Curve - Reconstruction
28. 8.6.7 CaseStudy2 - Download Code and Data
29. 8.7.0 Quiz
30. 8.7.1 Case Study 3 - Bitcoin Trading - Background
31. 8.7.2 Case Study 3 - Bitcoin Trading - Getting Started
32. 8.7.3 Case Study 3 - Bitcoin Trading - Data Preparation
33. 8.7.4 Case Study 3 - Bitcoin Trading - Algos and Models
34. 8.7.5 Case Study 3 - Bitcoin Trading - Visualisation
35. 8.7.6 Case Study 3 - Bitcoin Trading - Comparison
36. 8.7.7 CaseStudy3 - Download Code and Data
37. Quiz
38. ModuleAssignment
1. 9.1 Use cases in Finance
2. 9.2 Focus of this module
3. 9.3.1 Theory and Concepts - K means
4. 9.3.2 Theory and Concepts - hierarchical
5. 9.3.3 Theory and Concepts - Affinity propagation
6. 9.4.1 Clustering Master Template - Introduction and Getting Started
7. 9.4.2 Clustering Master Template - Data Preparation
8. 9.4.3 Clustering Master Template - K-means
9. 9.4.4 Clustering Master Template - Hierarchical
10. 9.4.5 Clustering Master Template - Affinity Propagation
11. 9.5.0 Quiz
12. 9.5.1 Case Study 1 - Pairs Trading - Background
13. 9.5.2 Case Study 1 - Pairs Trading - Getting Started
14. 9.5.3 Case Study 1 - Pairs Trading - K-Means
15. 9.5.4 Case Study 1 - Pairs Trading - Hierarchical
16. 9.5.5 Case Study 1 - Pairs Trading - Affinity Propagation
17. 9.5.6 Case Study 1 - Pairs Trading - Cluster Evaluation
18. 9.5.7 Case Study 1 - Pairs Trading - Pairs Selection
19. 9.5.8 Case Study 1 - Pairs Trading - Visualisation
20. 9.5.9 Case Study1 - Download Code and Data
21. 9.6.0 Quiz
22. 9.6.1 Case Study 2 - Investor's Clustering - Background
23. 9.6.2 Case Study 2 - Investor's Clustering - Getting Started
24. 9.6.3 Case Study 2 - Investor's Clustering - Algorithms and Models
25. 9.6.4 Case Study 2 - Investor's Clustering - Cluster Intuition
26. 9.6.5 Case Study2 - Download Code and Data
27. Quiz
28. ModuleAssignment
1. 10.1 Use cases in Finance
2. 10.2.1 RL Overview
3. 10.2.2 RL Components
4. 10.3.1 Framework Bellman Equations
5. 10.3.2 Framework Markov Decision Processes
6. 10.3.3 Framework Temporal Differences
7. 10.4.1 Models - Overview
8. 10.4.2 Models - Beyond Q learning
9. 10.5 Key Challenges of Reinforcement Learning
10. 10.6.0 Quiz
11. 10.6.1 Case Study 1 - Trading Strategy - Background
12. 10.6.2 Case Study 1 - Trading Strategy - Getting Started
13. 10.6.3 Case Study 1 - Trading Strategy - Model Setup
14. 10.6.4 Case Study 1 - Trading Strategy - Agent Script
15. 10.6.5 Case Study 1 - Trading Strategy - Model Training
16. 10.6.6 Case Study 1 - Trading Strategy - Model Testing
17. 10.6.7 Case Study 1 - Download Code and Data
18. 10.7.0 Quiz
19. 10.7.1 Case Study 2 - Derivatives Hedging - Background
20. 10.7.2 Case Study 2 - Derivatives Hedging - Getting Started
21. 10.7.3 Case Study 2 - Derivatives Hedging - Policy Gradient Model
22. 10.7.4 Case Study 2 - Derivatives Hedging - Model Training
23. 10.7.5 Case Study 2 - Derivatives Hedging - Model Testing Functions
24. 10.7.6 Case Study 2 - Derivatives Hedging - Model Results
25. 10.7.7 Case Study 2 - Derivatives Hedging - Model Summary
26. 10.7.8 Case Study 2 - Download Code and Data
27. Quiz
28. ModuleAssignment
1. 11.1 Use cases in Finance
2. 11.2 NLP - Python Packages
3. 11.3.1 NLP - Theory and Concepts - Preprocessing
4. 11.3.2 NLP - Theory and Concepts - Feature Representation
5. 11.3.3 NLP - Theory and Concepts - Inference
6. 11.4.0 Quiz
7. 11.4.1 Case Study 1 - Trading Strategy - Background
8. 11.4.2 Case Study 1 - Trading Strategy - Getting Started
9. 11.4.3 Case Study 1 - Trading Strategy - Data Preparation
10. 11.4.4 Case Study 1 - Trading Strategy - TextBlob
11. 11.4.5 Case Study 1 - Trading Strategy - Supervised Learning
12. 11.4.6 Case Study 1 - Trading Strategy - Unsupervised Learning
13. 11.4.7 Case Study 1 - Trading Strategy - Building the Strategy
14. 11.4.8 Case Study 1 - Trading Strategy - Strategy Results
15. 11.4.9 Case Study 1 - Download Code and Data
16. 11.5.0 Quiz
17. 11.5.1 Case Study 2 - Document Summarization - Background
18. 11.5.2 Case Study 2 - Document Summarization - Getting Started
19. 11.5.3 Case Study 2 - Document Summarization - Data Preparation
20. 11.5.4 Case Study 2 - Document Summarization - Model Training
21. 11.5.5 Case Study 2 - Document Summarization - Model Visualisation
22. 11.5.6 Case Study 2 - Download Code and Data
23. Quiz
24. NLP.ipynb
25. ModuleAssignment
1. 12 Summary

ML FINANCE COURSE

$250.00
243 lessons
8.5 hours of video content
11 Modules, 15+ Case Studies
20+ Quizzes and assignments
10000+ Lines of code

Reinvent yourself

Be a part of Machine Learning revolution in Finance

Purchase now

Meet Your Instructor

Jonathon Emerick

QuantPy Founder | UQ BE (Chemical) | UQ MFinMath

Jonathon is an Energy Trader with experience in quantitative risk analysis and valuation. He has an evolving YouTube channel related to quantitative finance and is enthusiastic about the applications of Machine Learning and AI in the financial industry.

Hariom Tatsat

VP, Barclays | Author | UC Berkeley MFE | IIT KGP

Extensive experience as a Quant in the areas of Predictive Modeling and Instrument Pricing. Co-author of the book "Machine Learning and Data Science Blueprints for Finance", published in December 2020 by O'Reilly. Machine Learning, AI and Fintech enthusiast.

Top Universities offer this course to their students.

This course was selected and trusted by universities and organizations worldwide.

What You'll Learn

★ Apply machine and deep learning models to solve real-world problems in finance.

★ Understand the theory and intuition behind several machine learning algorithms for regression, classification and clustering

★ Understand the underlying theory, intuition and mathematics behind Artificial Neural Networks (ANNs) and Deep Neural network.

★ Different machine learning based cutting-edge approaches to portfolio optimization.

★ Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.

★ Leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.

★ Use key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib for data plotting/visualization, and Keras, tensorflow for deep learning.

★ Assess the performance of trained machine learning regression models using various KPIs.

★ Train ANNs using back propagation and gradient descent algorithms.

★ Master feature engineering and data cleaning strategies for machine learning and data science applications.

Career Outlook

$115k

The average annual base pay for a Machine Learning in Finance roles in the US.

10 million

The anticipated machine learning experts needed in finance by 2026.

1.8 million

Current jobs is finance are at risk due to AI and machine learning.

Testimonials

“Whether you are a quantitative analyst in a hedge fund or investment banks looking to start building machine learning models in Python, or a machine learning student looking to work on a ML related project, look no further!”

Aman Kesarwani

“A really practical course. It has a GitHub code repo containing the python code for all case studies included with the course. The code can be easily customized for related ML/AI problems in Finance.”

John Larson

“Wonderfully organized and structured. The case studies to supplement theoretical explanation is something strong highlight of the course. ”

Matt Brandon

Who Should Take The Course

★ Buy/sell side quants	★ Asset/Wealth Managers
★ CXOs	★ Data Scientists
★ Machine Learning Engineers	★ Students targeting finance sector
★ Business Analysts	★ AI/ML enthusiasts

Frequently Asked Questions

What level of machine learning knowledge is needed to work as machine learning or data science practitioner in the finance industry?

Typically, industrial solutions in finance are simpler as compared to the cutting-edge research work going in the field of machine learning and AI. Overall, focus in the finance industry is more on the practical issues and customizing the tools and framework available to suit the requirement of the problem at hand, rather than coming up with cutting edge models. Hence, individuals with backgrounds in computer science, statistics, maths, financial engineering, econometrics and natural sciences should be able to reinvent themselves to work as machine learning experts in the finance industry.
Which machine learning algorithms are used in Finance?

All three kinds of machine learning algorithms including supervised, unsupervised and reinforcement learning are used in finance. Although most of the literature and discussion so far has been around supervised learning, unsupervised and reinforcement learning are also picking up pace in terms of use cases in finance. Additionally, NLP, which is a subset of AI and shares some common algorithms with machine learning, is currently used extensively in finance.
There are a lot of terms like machine learning, deep learning, AI and data science. What is the difference between them?

Deep learning is a subset of machine learning and machine learning is a subset of AI (Artificial Intelligence). Data science although is not a subset of machine learning but there are a lot of common elements between data science and machine learning. All these areas are extensively used in finance.
Do machine learning models require a lot of coding? What about the implementation and computation required for training these models?

Many programming languages, especially Python, provide methods and ways to implement machine learning models in a few lines of code. Some of the libraries in Python, especially scikit-learn and keras provide easier methods to implement deep learning algorithms, perform data processing and visualization. The training of the deep learning models can easily be performed using GPU and cloud services. The machine learning concepts and the steps in the case studies throughout the book come with detailed python code and related explanation.
Reinforcement Learning has lead to a breakthrough in gaming and other fields, what about finance?

The reinforcement learning algorithms that empowered “AlphaGo” are also finding inroads into finance. Reinforcement learning’s main idea of “maximizing the rewards” aligns beautifully with the core motivation of several areas within finance including algorithms trading and portfolio management.
How do we use unsupervised learning in finance?

There is a saying “If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake” which summarizes the importance of unsupervised learning, which is applicable to finance as well. Unsupervised learning models are categorized as clustering or dimensionality reduction models and are used across many areas in finance.
Is there a refund available?

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For more details on the refund policies see Click Here
Is there any support available after I purchase the course?

Yes, you can ask your queries related to the course on the community. We try our best to reply to the questions asap. However, we might need 2-3 business days in answering the questions. It might take longer in case of complicated questions. Additionally, the python ecosystem, APIs and the functions keep on changing quite frequently. Although, we try to be up to date with the latest setup, but some issues due to the changing python ecosystem is expected.
Will I be getting a certificate post the completion of the programme?

Yes. We provide the certificate after completion of all the quizzes in the course.

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Machine Learning In Finance (2024)

Financial Applications Coverage

Supervised Learning

Unsupervised Learning

Reinforcement Learning and NLP

Python Tools. and Packages

Free financial data - access and usage

11 modules

10000+ lines of code

Course Certificate

Webinars

Community

Affordable

ML FINANCE COURSE

$115k

10 million

1.8 million