Python Training Designed For Finance Professionals
Double the Accuracy of Your Predictive Models
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Unlock the power of Python and Machine Learning in finance with our comprehensive certification program.
*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.
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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
Learn how to apply Python and Machine Learning in the world of finance.
How it Works
Python Fundamentals
Applied Machine Learning
Orientation
Get setup in your coding environment and start writing your first lines of code in under 30 minutes. Setup your environment and quickly understand how to command basic functions that you’ll build on throughout the course.
Build on your existing foundation with excel analogs
By relating lessons and techniques to concepts finance professionals are already familiar with, PyFi courses deliver higher retention rates and a more efficient learning process. You are already a logical thinker and problem solver which is demonstrated by your work in Excel. PyFi builds off this foundation.
Practical application
The Python programming language is an immense topic to cover. There are many things you can learn and even more ways you can apply Python which are not relevant to you as a finance professional. PyFi teaches you what you need to know to be effective in your work without needing to pursue a formal computer science degree.
Apply proven solutions to your work
Because Python is open source, you’re able to apply working libraries, or frameworks, to your work with simple commands. Imagine downloading sophisticated financial models you could simply plug your data into. That’s exactly what’s possible with the Python programming language and PyFi gets you started by plugging the NumPy and Pandas library directly into your financial analysis.
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 Pipelines
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.
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
Python Fundamentals
Applied Machine Learning
Become familiar and comfortable with coding envrionments
- Use Jupyter Notebook to write and execute code
- Perform calculations
- Generate outputs with static text and dynamic values
- Create and manipulate variables
Identify common types of Python objects
- Create and manipulate important data structures
- Lists, Tuples, Sets, and Dictionaries
Use python arguments
- Create custom functions
- Repeat tasks through iterable objects using for loops
- Incorporate conditional logic using if statements
Become familiar with Python libraries starting with NumPy
- Create and manipulate NumPy arrays, a new type of Python object
- Perform special NumPy array math and aggregation functions
- Leverage NumPy's useful randomization tools
Become familiar with the Pandas library
- Create and manipulate Pandas DataFrames
- Create and manipulate Pandas Series
- Filter data using boolean masks
- Segment and summarize data by class with groupbys
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.
Career Paths
JP Morgan has recently stated and decided that all incoming analysts and associates will learn Python.
Risk Management
Offering solutions to manage various types of financial risk, including interest rate risk, currency risk, and commodity price risk. This might involve structuring derivatives, swaps, and hedging strategies.
Asset Management
Python and ML can be used to analyze market data, optimize portfolio allocations, and enhance investment strategies. These technologies enable asset managers to identify trends and make data-driven investment decisions.
Financial Analysis and Modeling
Financial analysts leverage Python for data analysis and financial modeling. ML can be applied to predict financial outcomes, evaluate investment opportunities, and perform scenario analysis.
Regulatory Compliance
Compliance officers use Python and ML to monitor transactions, detect fraudulent activities, and ensure compliance with regulatory requirements. ML algorithms can automate the detection of anomalous behavior, reducing the risk of financial crime and regulatory penalties.
Research
Researchers in investment banking use Python and ML to analyze financial markets, economic trends, and company fundamentals. This analysis supports investment decisions and strategy development.
Sales and Trading
While distinct from algorithmic trading, sales and trading professionals can use Python and ML for predictive analytics to forecast market movements, analyze client behavior, and optimize trading strategies.
Wealth Management
Advisors can use ML to provide personalized investment advice and portfolio management services, analyzing clients' financial situations, preferences, and risk tolerance to tailor investment strategies.
Market Strategy and Operations
Professionals in this area can use Python and ML to analyze market conditions, optimize operational processes, and improve efficiency. This includes everything from streamlining back-office operations to forecasting market demand.
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.
What Our Customers Are Saying:
"An incredible strength of Machine Learning Edge is the real world examples it brings into play. These are not some hypothetical applications of the material that are taken out of a textbook; these are real world, concrete, and ready for application in the student’s career. Every piece of information you are given is valuable and ready to be put to use."
Gage C.
Mathematics Undergraduate Student
"Machine Learning Edge is a fantastic introduction to machine learning applications of finance. It provides you with an excellent road map all the way from python basics to advanced machine learning algorithms. I constantly see new ways to apply what I have learned to projects in the workplace every day.
Will N.
Investment Banking Analyst
"If you are in the finance industry, or even a student aspiring to break in, Machine Learning Edge is absolutely essential. It is no secret that coding is the literacy of today, and MLE goes above and beyond in equipping you with the coding and machine learning knowledge increasingly demanded in the finance industry."
Ralph R.
Investment Banking Analyst
"Machine Learning Edge content is carefully curated to maximize retention and effectively bridges the gap between developed financial and data science skill-sets. Wall Street, meet Silicon Valley."
Jack V.
Debt Capital Markets Analyst
"I was very impressed with the course. As a buy-side investment professional, I can definitely see how this course could be used to not only make oneself more marketable, but to actually improve investment decisions and performance."
Brad B.
Private Equity Associate
"Machine Learning Edge is a rare find because it is tailored for busy finance professionals.As an analyst at a major investment bank, I was able to take several Python and Machine Learning techniques from this course and use them to improve relevant projects."
Terrence Z.
Investment Banking Analyst
Pricing Plan
Simplified pricing made to scale with your organization.
*Strike price reflects relative value and not msrp.
Meet Zach Washam
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.
