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.
Enrol NowMachine Learning concepts customized to Finance
Separate modules for each AI and Machine Learning Type with exhaustive concepts.
Supervised Learning
Unsupervised Learning
Reinforcement Learning and NLP
15+ Real-World Practical Applications
Case studies along with their python-based implementation.
Financial Applications Coverage
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
Financial data access & usage
Meet Your Instructors
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.
ML in Finance Course
- 11 modules
- 242 lessons
- 11 Modules, 15+ Case Studies
- 10+ hours of lectures
- 10000+ lines of code
- Quizzes
- Webinars
- Community
- Affordable
Course Curriculum
▶️ 1.1 Welcome
▶️ 1.2 Course Structure
▶️ 1.3 Getting the most out of it
✅ Coursecode
▶️ 2.1 Application of ML in Finance – Introduction
▶️ 2.2 Application of ML in Finance – Applications
▶️ 2.3 Types of Machine Learning
✅ Quiz
✅ Quiz
▶️ 3.3.1 Machine Learning Modelling Steps – Problem Definition
▶️ 3.3.2 Machine Learning Modelling Steps – Loading the data
▶️ 3.3.3 Machine Learning Modelling Steps – Data analysis
▶️ 3.3.4 Machine Learning Modelling Steps – Data preparation
▶️ 3.3.5 Machine Learning Modelling Steps – Evaluate models
▶️ 3.3.6 Machine Learning Modelling Steps – Model tuning
▶️ 3.3.7 Machine Learning Modelling Steps – Finalize the model
▶️ 4.1 Architecture
▶️ 4.2 Training
▶️ 4.3 Hyperparameters
✅ Quiz
▶️ 4.4.1 Creating an ANN in Python – Processing the Dataset
▶️ 4.4.2 Creating an ANN in Python – Building the ANN
▶️ 4.4.3 Creating an ANN in Python – ANN Training and Evaluation
▶️ 5.1 How is Supervised Learning used in Finance?
▶️ 5.2 Types of Supervised Learning
▶️ 5.3 Linear Regression
▶️ 5.4 Regularized Regression
▶️ 5.5 Logistic Regression
▶️ 5.6 Support Vector Machines
▶️ 5.7 K-Nearest Neighbors
▶️ 5.8 Linear Discriminant Analysis
▶️ 5.9 Classification and Regression Trees
▶️ 5.10 Introduction to Ensemble Methods
▶️ 5.10.1 Random Forest
▶️ 5.10.2 Extra Trees
▶️ 5.10.3 Adaptive Boosting
▶️ 5.10.4 Gradient Boosting Method
▶️ 5.11 Artificial Neural Networks
▶️ 5.12 Model Selection
▶️ 5.13 Model Performance
✅ Quiz
▶️ 6.1 Use cases in Finance
▶️ 6.2 Relationship with time series models
▶️ 6.3.1 Overview of time series model – components of a time series
▶️ 6.3.2 Overview of time series model – autocorrelation and stationarity
▶️ 6.3.3 Overview of time series model – traditional times series models
▶️ 6.4 Converting time series models to supervised learning models
▶️ 6.5.1 Regression and Time Series Master Template – Introduction
▶️ 6.5.2 Regression and Time Series Master Template – Getting Started
▶️ 6.5.3 Regression and Time Series Master Template – Data Analysis
▶️ 6.5.4 Regression and Time Series Master Template – Data Preparation
▶️ 6.5.5 Regression and Time Series Master Template – Algorithms and Models
▶️ 6.5.6 Regression and Time Series Master Template – Model Tuning
▶️ 6.5.7 Regression and Time Series Master Template – Finalize Model
▶️ 6.6.1 Using Deep Learning models for Time series – Overview
▶️ 6.6.2 Using Deep Learning models for Time series – RNN and LSTM
✅ Quiz
▶️ 6.7.1 Case Study 1 – Predicting Stock Price – Background
▶️ 6.7.2 Case Study 1 – Predicting Stock Price – Getting Started
▶️ 6.7.3 Case Study 1 – Predicting Stock Price – Data Analysis
▶️ 6.7.4 Case Study 1 – Predicting Stock Price – Data Preparation
▶️ 6.7.5 Case Study 1 – Predicting Stock Price – Algorithms and Models
▶️ 6.7.6 Case Study 1 – Predicting Stock Price – Model Tuning
▶️ 6.7.7 Case Study 1 – Predicting Stock Price – Finalize Model
▶️ 6.7.8 Case Study 1 – Download Code and Data
✅ Quiz
▶️ 6.8.1 Case Study 2 – Pricing a Derivative – Background
▶️ 6.8.2 Case Study 2 – Pricing a Derivative – Getting Started
▶️ 6.8.3 Case Study 2 – Pricing a Derivative – Data Analysis
▶️ 6.8.4 Case Study 2 – Pricing a Derivative – Data Preparation
▶️ 6.8.5 Case Study 2 – Pricing a Derivative – Algorithms and Models
▶️ 6.8.6 Case Study 2 – Pricing a Derivative – Model Tuning
▶️ 6.8.7 Case Study 2 – Pricing a Derivative – Finalize Model
▶️ 6.8.8 Case Study 2 – Download Code and Data
✅ Quiz
▶️ 6.9.1 Case Study 3 – Investor Risk Tolerance – Background
▶️ 6.9.2 Case Study 3 – Investor Risk Tolerance – Getting Started
▶️ 6.9.3 Case Study 3 – Investor Risk Tolerance – Data Preparation
▶️ 6.9.4 Case Study 3 – Investor Risk Tolerance – Feature Selection
▶️ 6.9.5 Case Study 3 – Investor Risk Tolerance – Algos and Models
▶️ 6.9.6 Case Study 3 – Investor Risk Tolerance – Model Tuning
▶️ 6.9.7 Case Study 3 – Investor Risk Tolerance – Finalize Model
▶️ 6.9.8 Case Study 3 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 7.1 Use cases in Finance
▶️ 7.2 Focus of this module
▶️ 7.3.1 Classification Master Template – Introduction
▶️ 7.3.2 Classification Master Template – Getting Started
▶️ 7.3.3 Classification Master Template – Data Analysis
▶️ 7.3.4 Classification Master Template – Data Preparation
▶️ 7.3.5 Classification Master Template – Algorithms and Models
▶️ 7.3.6 Classification Master Template – Model Tuning
▶️ 7.3.7 Classification Master Template – Finalize Model
▶️ 7.4.1 Case Study 1 – Fraud Detection – Background
▶️ 7.4.2 Case Study 1 – Fraud Detection – Getting Started
▶️ 7.4.3 Case Study 1 – Fraud Detection – Data Preparation
▶️ 7.4.4 Case Study 1 – Fraud Detection – Algorithms and Models
▶️ 7.4.5 Case Study 1 – Fraud Detection – Model Tuning
▶️ 7.4.6 Case Study 1 – Fraud Detection – Finalize Model
▶️ 7.4.7 CaseStudy1 – Download Code and Data
✅ Quiz
▶️ 7.5.1 Case Study 2 – Loan Default Probability – Background
▶️ 7.5.2 Case Study 2 – Loan Default Probability – Getting Started
▶️ 7.5.3 Case Study 2 – Loan Default Probability – Data Preparation
▶️ 7.5.4 Case Study 2 – Loan Default Probability – Feature Engineering
▶️ 7.5.5 Case Study 2 – Loan Default Probability – Algorithms and Models
▶️ 7.5.6 Case Study 2 – Loan Default Probability – Model Tuning
▶️ 7.5.7 Case Study 2 – Loan Default Probability – Finalize Model
▶️ 7.5.8 CaseStudy2 – Download Code and Data
✅ Quiz
▶️ 7.6.1 Case Study 3 – Bitcoin Trading Strategy – Background
▶️ 7.6.2 Case Study 3 – Bitcoin Trading Strategy – Getting Started
▶️ 7.6.3 Case Study 3 – Bitcoin Trading Strategy – Data Preparation
▶️ 7.6.4 Case Study 3 – Bitcoin Trading Strategy – Algorithms and Models
▶️ 7.6.5 Case Study 3 – Bitcoin Trading Strategy – Model Tuning
▶️ 7.6.6 Case Study 3 – Bitcoin Trading Strategy – Backtesting Model
▶️ 7.6.7 CaseStudy3 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 8.1 Use cases in Finance
▶️ 8.2 Focus of this module
▶️ 8.3.1 Theory and concepts – PCA
▶️ 8.3.2 Theory and concepts – SVD
▶️ 8.3.3 Theory and concepts – kPCA
▶️ 8.3.4 Theory and concepts – tSNE
▶️ 8.4.1 Dimensionality Reduction Master Template – Intro
▶️ 8.4.2 Dimensionality Reduction Template – Data Prep
▶️ 8.4.3 Dimensionality Reduction Master Template – PCA
▶️ 8.4.4 Dimensionality Reduction Master Template – SVD
▶️ 8.4.5 Dimensionality Reduction Master Template – KPCA
▶️ 8.4.6 Dimensionality Reduction Master Template – t-SNE
✅ Quiz
▶️ 8.5.1 Case Study 1 – Portfolio Management – Background
▶️ 8.5.2 Case Study 1 – Portfolio Management – Getting Started
▶️ 8.5.3 Case Study 1 – Portfolio Management – Data Prep
▶️ 8.5.4 Case Study 1 – Portfolio Management – Models
▶️ 8.5.5 Case Study 1 – Portfolio Management – Portfolio Sel
▶️ 8.5.6 Case Study 1 – Portfolio Management – Backtesting
▶️ 8.5.7 Case Study 1 – Download Code and Data
✅ Quiz
▶️ 8.6.1 Case Study 2 – Yield Curve – Background
▶️ 8.6.2 Case Study 2 – Yield Curve – Getting Started
▶️ 8.6.3 Case Study 2 – Yield Curve – Data Analysis
▶️ 8.6.4 Case Study 2 – Yield Curve – Data Preparation
▶️ 8.6.5 Case Study 2 – Yield Curve – PCA
▶️ 8.6.6 Case Study 2 – Yield Curve – Reconstruction
▶️ 8.6.7 Case Study 2 – Download Code and Data
✅ Quiz
▶️ 8.7.1 Case Study 3 – Bitcoin Trading – Background
▶️ 8.7.2 Case Study 3 – Bitcoin Trading – Getting Started
▶️ 8.7.3 Case Study 3 – Bitcoin Trading – Data Preparation
▶️ 8.7.4 Case Study 3 – Bitcoin Trading – Algos and Models
▶️ 8.7.5 Case Study 3 – Bitcoin Trading – Visualisation
▶️ 8.7.6 Case Study 3 – Bitcoin Trading – Comparison
▶️ 8.7.7 Case Study 3 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 9.1 Use cases in Finance
▶️ 9.2 Focus of this module
▶️ 9.3.1 Theory and Concepts – K means
▶️ 9.3.2 Theory and Concepts – Hierarchical
▶️ 9.3.3 Theory and Concepts – Affinity propagation
▶️ 9.4.1 Clustering Master Template – Introduction and Getting Started
▶️ 9.4.2 Clustering Master Template – Data Preparation
▶️ 9.4.3 Clustering Master Template – K-means
▶️ 9.4.4 Clustering Master Template – Hierarchical
▶️ 9.4.5 Clustering Master Template – Affinity Propagation
✅ Quiz
▶️ 9.5.1 Case Study 1 – Pairs Trading – Background
▶️ 9.5.2 Case Study 1 – Pairs Trading – Getting Started
▶️ 9.5.3 Case Study 1 – Pairs Trading – K-Means
▶️ 9.5.4 Case Study 1 – Pairs Trading – Hierarchical
▶️ 9.5.5 Case Study 1 – Pairs Trading – Affinity Propagation
▶️ 9.5.6 Case Study 1 – Pairs Trading – Cluster Evaluation
▶️ 9.5.7 Case Study 1 – Pairs Trading – Pairs Selection
▶️ 9.5.8 Case Study 1 – Pairs Trading – Visualisation
▶️ 9.5.9 Case Study1 – Download Code and Data
✅ Quiz
▶️ 9.6.1 Case Study 2 – Investor’s Clustering – Background
▶️ 9.6.2 Case Study 2 – Investor’s Clustering – Getting Started
▶️ 9.6.3 Case Study 2 – Investor’s Clustering – Algorithms and Models
▶️ 9.6.4 Case Study 2 – Investor’s Clustering – Cluster Intuition
▶️ 9.6.5 Case Study2 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 10.1 Use cases in Finance
▶️ 10.2.1 RL Overview
▶️ 10.2.2 RL Components
▶️ 10.3.1 Framework Bellman Equations
▶️ 10.3.2 Framework Markov Decision Processes
▶️ 10.3.3 Framework Temporal Differences
▶️ 10.4.1 Models – Overview
▶️ 10.4.2 Models – Beyond Q learning
▶️ 10.5 Key Challenges of Reinforcement Learning
✅ Quiz
▶️ 10.6.1 Case Study 1 – Trading Strategy – Background
▶️ 10.6.2 Case Study 1 – Trading Strategy – Getting Started
▶️ 10.6.3 Case Study 1 – Trading Strategy – Model Setup
▶️ 10.6.4 Case Study 1 – Trading Strategy – Agent Script
▶️ 10.6.5 Case Study 1 – Trading Strategy – Model Training
▶️ 10.6.6 Case Study 1 – Trading Strategy – Model Testing
▶️ 10.6.7 Case Study 1 – Download Code and Data
✅ Quiz
▶️ 10.7.1 Case Study 2 – Derivatives Hedging – Background
▶️ 10.7.2 Case Study 2 – Derivatives Hedging – Getting Started
▶️ 10.7.3 Case Study 2 – Derivatives Hedging – Policy Gradient Model
▶️ 10.7.4 Case Study 2 – Derivatives Hedging – Model Training
▶️ 10.7.5 Case Study 2 – Derivatives Hedging – Model Testing Functions
▶️ 10.7.6 Case Study 2 – Derivatives Hedging – Model Results
▶️ 10.7.7 Case Study 2 – Derivatives Hedging – Model Summary
▶️ 10.7.8 Case Study 2 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 11.1 Use cases in Finance
▶️ 11.2 NLP – Python Packages
▶️ 11.3.1 NLP – Theory and Concepts – Preprocessing
▶️ 11.3.2 NLP – Theory and Concepts – Feature Representation
▶️ 11.3.3 NLP – Theory and Concepts – Inference
✅ Quiz
▶️ 11.4.1 Case Study 1 – Trading Strategy – Background
▶️ 11.4.2 Case Study 1 – Trading Strategy – Getting Started
▶️ 11.4.3 Case Study 1 – Trading Strategy – Data Preparation
▶️ 11.4.4 Case Study 1 – Trading Strategy – TextBlob
▶️ 11.4.5 Case Study 1 – Trading Strategy – Supervised Learning
▶️ 11.4.6 Case Study 1 – Trading Strategy – Unsupervised Learning
▶️ 11.4.7 Case Study 1 – Trading Strategy – Building the Strategy
▶️ 11.4.8 Case Study 1 – Trading Strategy – Strategy Results
▶️ 11.4.9 Case Study 1 – Download Code and Data
✅ Quiz
▶️ 11.5.1 Case Study 2 – Document Summarization – Background
▶️ 11.5.2 Case Study 2 – Document Summarization – Getting Started
▶️ 11.5.3 Case Study 2 – Document Summarization – Data Preparation
▶️ 11.5.4 Case Study 2 – Document Summarization – Model Training
▶️ 11.5.5 Case Study 2 – Document Summarization – Model Visualisation
▶️ 11.5.6 Case Study 2 – Download Code and Data
✅ Quiz
💻 ModuleAssignment
▶️ 12.1 Summary
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.
Why I started an online Course?
The QuantPy Story
If you don't know me, my is Jonathon Emerick and I started a YouTube channel called QuantPy. I noticed a lack of video style quantitative finance resources online, and decided to embark on the journey of creating online tutorials and documenting my journey towards "becoming a Quant". The goal was to follow my interests in quantitative finance and attempt to help others along the way.
Collaboration with Hariom Tatsat
I never intended to make an online course, however at the end of 2021 I was introduced to Hariom Tatsat a co-author of the book "Machine Learning and Data Science Blueprints for Finance". Hariom has extensive market knowledge and experience in applying machine learning to many financial applications, having done so throughout his career working at large Investment Banks. Leaning on his years of experience and knowledge, he proposed creating a course that delivered a very complete and practical approach to the applications of machine learning in finance.
My Contribution
Along with my ability to explain complex financial topics simply and effectively, I have a strong interest in the potential applications of Machine Learning and AI within both my own industry, the energy industry, and the financial industry into the future. Hence I was happy to enbark on this collaboration, both for solidifying concepts I was introduced to while studying Financial Mathematics at University, as well as learning from the extensive experience Hariom has gained throughout his career in applying machine learning to real world problems.
Our Goal
We developed this course with an emphasis on providing value first and foremost. Therefore, we hope to have not only delivered a complete and practical course on Machine Learning in Finance, but most importantly an affordable course, having designed this program keeping students in mind. We know you'll appreciate the amount of value and insight provided by Hariom's experience and access to 15+ case studies with over 10,000+ lines of code applying machine learning to real world financial problems. For more details, please read the Machine Learning in Finance section of this website.
Frequently asked questions
Most frequent questions and answers
What level of machine learning knowledge is needed to work as a machine learning or data science practitioner in the financial 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.
Do machine learning model 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.
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.
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.
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 Here
Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community.
Will I be getting a certificate post the completion of the programme?
It is a self-paced course and currently no certificates are available. We will provide the certificate in the subsequent version of the course.