Applied Machine Learning

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Applied Machine Learning

$169.00 USD

$247.00 USD

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Applied Machine Learning is an exciting journey into the real deal, machine learning in the world of finance. In this course, you’re going to navigate through several new useful libraries and Machine Learning techniques and then work to build two algorithms that have been used to advise Fortune 500 companies including McDonalds, Tiffanys, Gap, and more. * Some code adjusted to protect original IP *

Course Content

Data Cleaning & Exploration

Your machine learning models can only be as good as your training data. In this lesson, you will learn how to find and correct errors in your datasets using the datavisualization packages Matplotlib and Seaborn.

Regression Algorithms

Learn about regression problems and 5 machine learning algorithms used to solve them. Those models are Lasso, Ridge, and Elastic Net, which fall under the cateogry of Regularized Regression Models. The other models you will learn are Random Forest and Gradient Boosting which fall under the category of Decision Tree Ensemble Models. You will also learn about overfitting and the tools at your disposal to prevent it.

Liquidity Regressor Case Study

Build your first machine learning model from start to finish. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to advise companies such as: HP, McDonalds, Nordstrom, and many more.

Classification Algorithms

Learn about classification problems and some machine learning algorithms used to solve them. By the end of this lesson, you will be familiar with

Characteristics of Binary Classification Problems
Regularized Logistic Regression Models
Decision Tree Ensemble Classification Models

Investor Classifier Case Study Pt I

Explore the data for your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.

Investor Classifier Case Study Pt II

In this lesson, you will complete your second machine learning model. The model you will construct is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients.

Professional Certification Exam

Verify Your Learning: Put your new machine learning skills to the test and see what you have learned and ensure retention.

Boost Your Resume: Add an impressive certification to your resume to demonstrate your new skills to employers.

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*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming and its application in the world of finance.

Why is Python important in Finance?

Automate
Automate the repetitive - Stop manually updating reports, reconciling data, or reformatting spreadsheets. Python does it for you.
Enhance
Enhance Excel, don't replace it - Write Python scripts that work alongside Excel to speed up calculations, automate models, and fetch real-time data.
Integrate
Integrate across platforms - Move data seamlessly between Oracle, PowerBI, Tableau, SQL databases, Excel, etc.
Accurate
More accurate forecasting & analytics - Reduce human error and leverage Python's statistical libraries to improve financial models.
Decisions
Make better decisions, faster - Turn raw data into insights with Python's data visualization and reporting capabilities

FP_A_Workflows_Pedro_-_visual_selection_2

Python scripts integrate SAP Analytics Cloud with Tableau by leveraging the SAP Analytics Cloud OData API. Python fetches real-time financial data, cleans it, and sends it to Tableau for visualization. This integration reduces reporting cycle time by an estimated 40% while ensuring accurate and up-to-date dashboards.

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Investment_Banking_Workflows_-_visual_selection_3

Extract financial and bond data from Bloomberg and automatically calculate key financial ratios like debt-to-equity and interest coverage. Machine learning models predict default probabilities. The models are validated for accuracy and risk scores and probability metrics are exported to Excel for presentation. Save approximately 15 hours while improving the accuracy of your forecasts by 25% with this Python integration.

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Private_Equity_Workflows_-_visual_selection

Python automates the consolidation of financial data from portfolio companies, reducing manual aggregation and saving approximately 6 hours per month. IT streamlines KPI calculations, eliminating Excel formula errors and improves accuracy by 30%. Automated benchmarking and real-time alerts highlight underperformers, cutting approx. 4 hours of manual comparisons. Dynamic dashboards in Tableau replace static Excel chats, saving an additional 3 hours per month. Overall, this integration saves 13 hours monthly and improves accuracy by 30%.

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Unsure if Python, or PyFi is right for you?

Hear from PyFi students. Be Certain in your decision.

The PyFi Guarantee

We offer a 100%, no questions asked, 30 day money-back guarantee to completely de-risk your investment. Enroll now, and if for any reason you aren't completely satisfied, send us an email and you'll get a full refund for up to one month after enrollment.

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How does the Applied Machine Learning course work?

Prepare Your Data

If you want a great machine learning algorithm, you need great training data. Correct errors, eliminate sparse classes, and remove unwanted observations to provide your algorithm premium fuel.

Build Your Piplines

Model pipelines tell your algorithm how to process your data. Construct pipelines that standardize your data to a common scale, define competing model classes, and specify random states.

Train & Tune Your Algorithm

Optimizing your machine learning algorithm is like tuning a race car. Train competing models and tune your hyperparameters using cross-validation to maintain the integrity of your testing data.

Select the Winning Model

Your algorithm competes against itself to produce optimal results. After training and tuning your models, you can select the winning model and use it to make superior predictions.

Curriculum

Sample Lesson Below

Applied Machine Learning is approximately 3.5 hours in length each. A student can generally complete the course in 6-10 hours when working through all of the exercises. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.

Data Cleaning & Exploration

Your machine learning models can only be as good as your training data. In this lesson, you will learn how to find and correct errors in your datasets using the data visualization packages Matplotlib and Seaborn.

Module Zip Files : Jupyter Notebook, Stock Data v2
ML 01.01 The ML Process
ML 01.02 Matplotlib and Seaborn
ML 01.03 A Few Quick Notes
ML 01.04 Exercise
ML 01.04 Solution
ML 01.05 .countplot()
ML 01.06 Exercise
ML 01.06 Solution
ML 01.07 Replace & Sparse Classes
ML 01.08 Exercise
ML 01.08 Solution
ML 01.09 Exercise
ML 01.09 Solution
ML 01.10 Spotting Outlier
ML 01.11 Exercise
ML 01.11 Solution
ML 01.12 Exercise
ML 01.12 Solution
ML 01.13 Exercise
ML 01.13 Solution
ML 01.14 Exercise
ML 01.14 Solution
ML 01.15 Exercise
ML 01.15 Solution
ML 01.16 NaN Object
ML 01.17 Exercise
ML 01.17 Solution
ML 01.18 Dropping Null Values
ML 01.19 Exercise
ML 01.19 Solution
ML 01.20 Box Plots
ML 01.21 Exercise
ML 01.21 Solution
ML 01.22 Saving Your Dataframe
ML 01.23 Review

Regression Algorithms

Learn about regression problems and 5 machine learning algorithms used to solve them. Those models are Lasso, Ridge, and Elastic Net, which fall under the category of Regularized Regression Models. The other models you will learn are Random Forest and Gradient Boosting which fall under the category of Decision Tree Ensemble Models. You will also learn about overfitting and the tools at your disposal to prevent it.

ML 01.02 What are Regression Algorithms
ML 02.02 Real Relationships and Overfitting
ML 02.03 Regularizations
ML 02.04 Decision Tree Ensemble Methods

Liquidity Regressor Case Study

Module ZipFiles: Liquidity_client.csv, Liqudiity_data.csv, ML 03 Jupyter Notebook
ML 03.01 Case Study Overview
ML 03.02 Exercise
ML 03.02 Solution
ML 03.03 Metadata
ML 03.04 Exercise
ML 03.04 Solution
ML 03.05 Splitting Your Data
ML 03.06 Exercise
ML 03.06 Solution
ML 03.07 train_test_split()
ML 03.08 Unpacking Lists
ML 03.09 Exercise
ML 03.09 Solution
ML 03.10 Progress Checkpoint
ML 03.11 Model Pipelines
ML 03.12 Exercise
ML 03.12 Solution
ML 03.13 Progress Checkpoint
ML 03.14 Hyperparameter Tuning
ML 03.15 Exercise
ML 03.15 Solution
ML 03.16 Exercise
ML 03.16 Solution
ML 03.17 Aggregating Hyperparameter Grids
ML 03.18 Progress Checkpoints
ML 03.19 Cross Validation
ML 03.20 Creating Untrained Models
ML 03.21 Exercise
ML 03.21 Solution
ML 03.22 Training and Tuning Models
ML 03.23 Exercise
ML 03.23 Solution
ML 03.24 Model Evaluation
ML 03.25 Exercise
ML 03.25 Solution
ML 03.26 Progress Checkpoint
ML 03.27 Visualizing Model Predictions
ML 03.28 Exercise
ML 03.28 Solution
ML 03.29 Using Your Model
ML 03.30 Review

Classification Algorithms

Learn about classification problems and some machine learning algorithms used to solve them. By the end of this lesson, you will be familiar with ▪ Characteristics of Binary Classification Problems ▪ Regularized Logistic Regression Models ▪ Decision Tree Ensemble Classification Models

ML 04.01 Binary Classification
ML 04.02 Logistic Regression
ML 04.03 Decision Tree Classifier

Investor Classifier Case Study Pt I

ML 05 Module Zip File: Investor_data.csv, Jupyter Notebook
ML 05.01 Case Study Overview
ML 05.02 Exercise
ML 05.02 Solution
ML 05.03 Metadata
ML 05.04 Exercise
ML 05.04 Solution
ML 05.05 One Error
ML 05.06 Exercise
ML 05.06 Solution
ML 05.07 Countplot of Investors
ML 05.08 Exercise
ML 05.08 Solution
ML 05.09 Exploring Relationships
ML 05.10 Exercise
ML 05.10 Solution
ML 05.11 Reviewing Your Results
ML 05.12 Feature Engineering
ML 05.13 Exercise
ML 05.14 Solution
ML 05.14 Reviewing Tier Change
ML 05.15 Controlling for Demotions
ML 05.16 Exercise
ML 05.16 Solution
ML 05.17 Analyzing Goldman Sachs
ML 05.18 Exercise
ML 05.18 Solution
ML 05.19 Implot()
ML 05.20 Exercise
ML 05.20 Solution
ML 05.21 Review

Investor Classifier Cast Study Pt II

ML 06 Module Zip File: Investor_data2.csv, Jupyter Notebook
ML 06.01 Import Packages and Data
ML 06.02 Exercise
ML 06.02 Solution
ML 06.03 Dummy Variables ML 06.04 Exercise
ML 06.04 Solution
ML 06.05 Remove Redundant Target
ML 06.06 Splitting Data
ML 06.07 Exercise
ML 06.07 Solution
ML 06.08 Model Pipeline
ML 06.09 Exercise
ML 06.09 Solution
ML 06.10 Validating Pipelines
ML 06.11 Hyperparameters
ML 06.12 Exercise ML 06.12 Solution
ML 06.13 Validating Hyperparameter Grids
ML 06.14 Cross Validation
ML 06.15 Exercise
ML 06.15 Solution
ML 06.16 Fitting Untrainer Models
ML 06.17 Exercise
ML 06.17 Solution
ML 06.18 AUROC
ML 06.19 Confusion Matrix
ML 06.20 Perfect AUROC
ML 06.21 Calculating AUROC
ML 06.22 Exercise
ML 06.22 Solution
ML 06.23 Review

Professional Certification Exam

Verify Your Learning: Put your new Machine Learning skills to the test to see what you have learned and ensure retention.

Boost Your Resume: Add an impressive certification to your resume to demonstrate your new skills to employers.

Jupyter Notebook (Exam Exercises)

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$169.00 USD

$247.00 USD

Lifetime access
Instructor Support
30-day Money Back Guarantee

Objectives

By the end of the course you will...

Understand how Python and Machine Learning can be used in the world of finance to double the accuracy of your predictive models and gain efficiencies in your work not possible with Excel alone

Identify overfit regression models

Master the skill of distinguishing overfit models that promise more than they deliver. By understanding the nuances of overfitting, you will be equipped to refine your models to ensure they perform reliably in real-world financial scenarios, enhancing their predictability and your confidence in their forecasts.

Compare different Machine Learning models

Dive deep into the world of regularized regression and decision tree ensemble algorithms. This course highlights the strengths and limitations of each, guiding you to make informed decisions on which algorithm to deploy for your financial modeling tasks, ensuring optimal balance and performance.

Explain the confusion matrix and its relation to the ROC curve

Gain clarity on evaluating model performance with an in-depth understanding of the confusion matrix and its critical role in financial predictions. Learn how it intertwines with the ROC curve to give you a comprehensive view of your model's accuracy, allowing you to enhance predictive models with precision.

Construct training data sets, testing data sets, and model pipelines

Learn to construct robust training and testing datasets and streamline your model development process with efficient pipelines. This skill set is crucial for developing models that are not only accurate but also scalable and adaptable to the fast-paced changes in the finance industry.

Perform advanced data cleaning, exploration, and visualization

Elevate your data preparation skills with advanced techniques in cleaning, exploring, and visualizing financial datasets. These foundational steps are key to uncovering insights and trends that drive successful machine learning models, setting the stage for impactful financial analysis and predictions.

Engineer features based on conditional relationships between existing features

Unlock the potential of your financial datasets by learning how to engineer features that reveal deeper insights. Understand the conditional relationships between features to create new, powerful predictors that enhance the performance of your machine learning models in predicting market movements and trends.

Build and finalize a machine learning classifier, and so much more…

Bring your financial prediction models across the finish line by mastering the art of building and finalizing machine learning classifiers. This course empowers you to not only construct but also fine-tune classifiers that stand up to the rigors of the finance world, ensuring your models are both accurate and resilient under market pressures.

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$169.00 USD

$247.00 USD

99% Satisfaction Rate
30-day Money Back Guarantee

linkedin_certificate_applied_machine_learning

Completion Certificates

After completing each course and passing the certification exam, students will be granted a PyFi Certification for that specific course. Use these certificates as a signal to employers that you have the technical skills to immediately add value to your team.

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Learn the same machine learning algorithms that were used to advise the following companies:

*Slight changes to the code made to protect IP

Meet Zach Washam,
PyFi Founder & Head of Instruction

While learning Python as an investment banker, Zach made an interesting observation: Python programming had a lot in common with the Excel models he made at work. By thinking of Python like Excel, Zach quickly learned the coding language and invented Wells Fargo Securities' first machine learning algorithm for investment banking and capital markets. ‍

After submitting two algorithms for patent protection and winning Wells Fargo's 2018 "Local Sphere Innovation Award," Zach left investment banking to launch PyFi. ‍

Now, Zach’s courses have been delivered to thousands of finance students and professionals around the world.

*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming and its application in the world of finance.

How Can We Help You?

What makes PyFi courses unique?

If you're looking to learn general Python this might not be the right class for you.

But if you'd rather understand how Python can be used in the world of finance, instead of building some irrelevant game, and if you'd like to learn the skills needed to build your own tools, then you're in the right place.

PyFi is built specifically for professionals in the world of finance and teaches you by relating concepts to excel so that you can learn faster and retain more of what you learn.

How long does it take to complete a Applied Machine Learning?

Applied Machine Learning is approximately 3.5 hours in length. A student can generally complete the course in 6-10 hours when working through all of the exercises. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.

How will learning Python help my finance career?

Firms are in the business of making profit, whether thats a bank, a fund, a corporation, etc. They hire you based on your ability to provide disproportionate value to them and their goals, a value that exceeds the cost of your salary. As technology changes, the level of demand of employee output continues to rise. In a pool of many talented candidates, having a competitive advantage that can make you more efficient and effective in your workflows and can make your predictive models more accurate means the value you are able to provide to your firm will be significantly greater than your contemporaries. Python is a competitive advantage in finance. In fact, more and more positions are preferring or requiring this skill to even be considered for the role in the first place.

What if I don't like the course?

PyFi offers a no questions asked, 30 day money-back guarantee. If you aren't completely satisfied with the course, send us an email and we will refund your money up to one month after you enroll.

How can we afford to do this? Because fewer than 1% of our customers ask for refunds. PyFi provides the best Python training on the market, and we know you are going to love it.

Do I need to work from a specific OS?

No. You can work through PyFi content from any OS.

Whats included in "Applied Machine Learning?"

Applied Machine Learning is a stand alone course. You will receive access to our "LMS" system where you will access the Jupyter Notebook files you'll be working through. You will also receive instructor support via the comment section of each video lesson.

Do I need to understand investing and portfolios to take the courses?

The finance specific topics that are discussed in the courses can be picked up as you work through each topic.

Have any questions? We’re ready to help!

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