Applied Machine Learning

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Applied Machine Learning

$129.00 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.

RBC_700530b9-0f2c-4b97-8b0b-3a42d6fc1244

*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 Python for Pri

Why are major companies like Amazon, Microsoft, Goldman Sachs, JP Morgan, Apollo,
Blackrock, etc., increasingly preferring or requiring Python for their finance employees?

Python outperforms Excel in three key areas.

Yellow_Full_Photo_Tech_Gadget_Review_YouTube_Thumbnail_5

Data Extraction & Transformation

Excel:

Manually download financial data from external sources (e.g., Bloomberg, ERP systems).
Use Excel to clean, transform, and organize data (e.g., currency conversions, date reformatting).
Save processed files into standardized templates for further analysis.

Estimated Time: 6-10 hours per dataset.

Python:

Use APIs (e.g., Bloomberg Terminal API, Alpha Vantage) or web scraping (e.g., "BeautifulSoup") to automate data extraction.
Process data using "Pandas" for transformations, such as currency normalization and date conversions, programmatically.
Export cleaned and transformed datasets directly to desired formats (e.g., CSV, SQL database).

Estimated Time: 1-2 hours for setup; ongoing runs take seconds.

Efficiency Gains:

Reduces data extraction and transformation time by 80-90%.
Ensured standardized outputs for all datasets greatly reducing the opportunity for human caused error.
Handles lager datasets with ease, accommodating diverse data sources.

Financial Reporting & Dashboards

Excel:

Compile data manually from various systems (e.g., accounting software, ERP).
Build static financial reports (e.g., P&L, balance sheet) using formulas and pivot tables.
Update reports periodically, requiring manual recalculations and formatting.

Estimated Time: 8-12 hours per reporting cycle.

Python:

Automate data aggregating using Python scripts to pull from APIs or databases.
Generate financial reports programmatically with libraries like "Matplotlib," "Plotly," or "OpenPyXL" for styled Excel outputs.
Build interactive dashboards using "Dash" or "Streamlit" for real-time financial insights.

Estimated Time: 2-3 hours to set up; updates occur in real time.

Efficiency Gains:

Reduces reporting time by 75-85%
Provides live dashboards with automatically refreshed data.
Delivers consistent and branded report formats.

Risk Management

Excel:

Manually collect data for risk metrics (e.g., portfolio exposures, market data) and enter into Excel.
Calculate key risk metrics (e.g., beta, standard deviation, VAR) using custom formulas.
Compile static risk reports and update chats for board presentations.

Estimated Time: 10-15 hours per risk assessment.

Python:

Automate data ingestion for market and portfolio data via APIs or database queries.
Calculate risk metrics programmatically using libraries like "scipy" and "NumPy."
Generate dynamic, interactive risk dashboards with "Plotly" or "Dash," including drill-down capabilities.

Estimated Time: 2-4 hours for setup; ongoing tasks occur in real time.

Efficiency Gains:

Reduces risk analysis and reporting time by 75-85%.
Automates complex calculations, minimizing errors.
Allows for more granular insights and real-time tracking.

Report Consolidation

Excel:

Manually collect individual reports from various teams or departments.
Copy data into a single workbook, adjusting formats and aligning metrics.
Use pivot tables or formulas to create a unified summary.

Estimated Time: 6-10 hours per reporting cycle.

Python:

Use "Pandas" to merge and align departmental data programmatically.
Automate formatting and metric alignment to ensure consistency.
Generate a consolidated report in Excel, PDF, or dashboard format directly using libraries like "XlsxWriter" or "Plotly."

Estimated Time: 2-3 hours for initial setup; updates occur in minutes.

Efficiency Gains:

Reduces reporting time by up to 70%
Ensures unified formatting and structure across all reports.
Handles more data sources and lager datasets efficiently.

Data Exploration and Trends

Excel:

Import data into Excel and create static charts (e.g., line charts, bar charts) to visualize historical trends, such as revenue growth or portfolio returns.
Use filters and slicers to explore subsets of data manually.
Customize chart formatting for reports, adjusting axes, labels and colors.

Estimated Time: 4-6 hours for initial setup and formatting; additional time needed for updates.

Python:

Use "Matplotlib" or "Seaborn" to programmatically generate detailed trend chats (e.g., revenue trends, ROI over time).
Enable exploration by building interactive widgets with "Plotly" or "Altair" for filtering and dynamic views.
Automate chat updates directly from a live data sources, eliminating the need for manual adjustments.

Estimated Time: 1-2 hours for initial setup; updates occur automatically.

Efficiency Gains:

Reduces chart creation and updates by 75-85%
Provides tools for real-time data slicing and exploration, enhancing insight generation.
Maintains professional, uniform visuals across datasets and reports.

Interactive Dashboards

Excel:

Use pivot tables and slicers to create static dashboards for financial metrics (e.g., revenue breakdowns or cost trends).
Manually adjust charts and layouts for each reporting period or aduience.
Refresh data manually, risking errors in linked formulas or connections.

Estimated Time: 6-10 hours per dashboard.

Python:

Build interactive dashboards using "Dash" or "Streamlit," allowing real-time filtering, chart updates, and user inputs.
Integrate dashboards riectly with live data sources (e.g., APIs, databases) to ensure real-time accuracy.
Include advanced features like drill-downs, trend forecasts, and customizable views for specific audiences.

Estimated Time: 3-5 hours for setup; updates occur in real time.

Efficiency Gains

Reduces dashboard creation time by up to 50%, with no ongoing update requirements.

Enables dynamic exploration and customization, far surpassing Excel's capabilities.
Connects to live data, ensuring all metrics are current and error-free.

Portfolio and Risk Visualizations

Excel:

Create static charts (e.g., pie charts for portfolio allocation, scatter plots for risk vs. return) manually, adjusting ranges and formatting repeatedly.
Use formulas and conditional formatting to highlight outliers or key risk areas.
Update charts and calculations manually as portfolio data changes.

Estimates Time: 5-8 hours per visualization set.

Python:

Use "Plotly" or "Matplotlib" to generate interactive visuals, such as dynamic scatter plots of risk-return profiles or heatmaps of portfolio diversification.
Automate visual updates directly from portfolio management systems or databases.
Add interactive features like tooltips, zooming, and filtering for deeper analysis.

Estimated Time: 2-3 hours for setup; updates occur automatically.

Efficiency Gains

Reduces manual effort by 60-70%, freeing time for deeper analysis.
Offers richer visual detail with interactivity, enabling better portfolio decisions.
Maintains up-to-date visuals as portfolio data evolves.

Waterfall Charts

Excel:

Manually calculate step-by-step changes in values (e.g., revenue to net income) and input these into chart data ranges.
Use Excel's waterfall chart feature, requiring manual adjustments for formatting and colors.
Repeat the process for each new dataset, rebuilding charts from scratch.

Estimated Time: 6-8 hours per chat cycle.

Python:

Use "Matplotlib" or "Plotly" to programmatically generate waterfall charts from data.
Automate the calculation of cumulative changes and visual formatting for presentation-ready outputs.
Build reusable scripts to create charts for multiple datasets or reporting periods without manual intervention.

Estimated Time: 2-3 hours to set up; new charts update in minutes.

Efficiency Gains:

Cuts chart creation time by 70-80%.
Reusable scripts eliminate repetitive work for future cycles.
Allows precise control over aesthetics and data handling.

Predictive Modeling

Excel:

Manually prepare datasets by importing data and cleaning it with formulas (e.g., "IF," "VLOOKUP," and "FILTER."
Build regression models using Excel's built-in tools like "Data Analysis ToolPak" or "LINEST,: often requiring multiple iterations to refine.
Validate models by creating separate sheets for testing and comparing predictions against actual results.

Estimated Time: 8-12 hours per model, including preparation, building, and testing.

Python:

Use "Pandas" to clean and prepare datasets programmatically.
Leverage libraries like "scikit-learn" to build and refine regression, classification, or clustering models efficiently.
Automate model validation with cross-validation techniques and metrics like "RMSE" or accuracy scores.

Estimated Time: 2-4 hours per model, depending on complexity.

Efficiency Gains:

Reduces time spent by 60-70%.
Handles lager datasets and more complex models with ease.
Automates testing and validation for higher reliability.

Risk Analysis and Credit Scoring

Excel:

Import and preprocess borrower data manually using formulas to alculate credit metrics like DSCR (Debt-Service Coverage Ratio) or credit utilization rates.
Develop scoring models using weighted averages or ranking methods.
Manually update credit scores when new data becomes available.

Estimated Time: 6-10 hours per analysis, including updates.

Python:

Automate data preprocessing with "Pandas," applying transformations and aggregations for key metrics.
Use machine learning models (e.g., decision trees or logistic regression in "scikit-learn") to predict default probabilities or assign credit scores dynamically.
Integrate scripts with real-time data feeds to update credit scoring automatically.

Estimated Time: 3-5 hours for setup; updates occur automatically.

Efficiency Gains:

Reduces analysis time by up to 50%.
Produces more robust scoring models through machine learning.
Eliminates manual recalculations for data updates greatly reducing the opportunity for human-error.

Debt Sustainability

Excel:

Create a spreadsheet for debt metrics, such as interest coverage ratio and leverage ratio, and manually input assumptions for scenarios.
Use Excel formulas and goal-seek tools to test sustainability under different conditions (e.g., interest rate hikes or revenue drops).
Manually adjust and record results for different scenarios, risking errors in formula and references.

Estimated Time: 8-12 hours per analysis.

Python:

Build automated models using "NumPy" or "Pandas" to calculate debt metrics and simulate sustainability scenarios programmatically.
Use Monte Carlo simulations with libraries like "SciPy" to assess risk under various assumptions.
Output results into structured reports or interactive dashboards for better decision-making.

Estimated Time: 2-4 hours for setup; scalability for additional scenarios is instantaneous.

Efficiency Gains:

Reduces effort by 70-80%
Tests thousands of scenarios quickly, enhancing depth of analysis.
Minimizes errors and ensures consistent results.

Revenue Projections

Excel:

Use historical data to calculate growth rates manually and build projection models with basic formulas (CAGR, TREND).
Apply static growth scenarios and manually adjust forecasts for variables like seasonality or market conditions.
Test sensitivity by duplicating sheets and running "what-if" scenarios manually.

Estimated Time: 10-15 hours for detailed projections with sensitivity analysis.

Python:

Automate data preprocessing with "Pandas" to clean and organize historical revenue data.
Apply advanced predictive techniques like "time-series forecasting" (e.g., ARIMA, Prophet) to generate accurate projections.
Conduct sensitivity analysis programmatically, varying multiple inputs and visualizing impacts dynamically.

Estimated Time: 3-5 hours for setup and analysis.

Efficiency Gains:

Reduces effort by 70-80%
Improves forecasts with advanced models like time-series analysis.
Simplifies running multiple scenarios without added manual effort.

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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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$247.00 USD

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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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$129.00 USD

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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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