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

    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

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

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.

Subscribe to Newsletter

Get educational content and details about latest developments.

Thank You