How does the MLE Professional Certification course work?
By the End of the Course, You Will Be Able To:
What's Inside
The courses are approximately 3.5 hours each and you have unlimited access once you’ve signed up. No programming skills required!
The Basics
Setting Up Your Coding Environment: We will walk you through setting up your coding environment step-by-step, including downloading the Anaconda distribution system and getting familiar with Jupyter Notebooks.
Performing Calculations: You will learn how to use Python to perform basic mathematical calculations.
Generating Outputs with Static Text and Dynamic Values: You will learn to incorporate text into your Python code and generate outputs with dynamic values.
Python Objects
Python Objects: Python is an object-oriented programming language, which means that Python code is organized around objects, or pieces of data that interact with each other based on instructions written in the code.
Data Structures: In this section, you will learn about data structures - types of Python objects that help you to organize other objects. Examples include lists, tuples, sets, and dictionaries.
Custom Functions
Creating Custom Functions: In addition to Python's built-in functions (similar to Excel's built-in functions), you can create custom functions that allow you to package and reuse your own custom code.
Repeating Tasks through Iterable Objects: Learn how to automate tedious tasks by repeating actions through iterable objects using variables and For Loops.
Incorporating Conditional Logic: Write more complex and useful applications that evaluate and respond to conditions that you define.
Numpy
Importing Open-Source Packages: Instead of building everything on your own, learn to leverage the work of others by importing third-party packages.
NumPy Arrays: Get familiar with creating and manipulating a powerful new type of Python object: The NumPy Array.
Powerful Statistical Tools: Use NumPy's powerful statistical functions to quickly and easily analyze large quantities of data.
Pandas
Two New Python Objects: Pandas provides you with two new Python objects, the DataFrame and the Series, which you can use to import and manipulate data from your Excel files.
Filtering Data with Boolean Masks: Use the Boolean object type to create indicator variables, test conditions, and filter your data.
Segmenting with Groupby: Segment and summarize your data across categories using the useful .groupby() function.
Preparing your Data
Correct errors, eliminate sparse classes, and remove unwanted observations to provide your algorithm premium fuel.
Build Your Pipelines
Construct pipelines that standardize your data to a common scale, define competing model classes, and specify random states.
Train & Tune Your Algorithm
Train competing models and tune your hyperparameters using cross-validation to maintain the integrity of your testing data.
Select the Winning Model
After training and tuning your models, you can select the winning model and use it to make superior predictions.
Certification Exam
Verify Your Learning: Put your new Python 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.
Complete the Prerequisite to Applied Machine Learning: After completing Python Fundamentals, you will have the necessary prerequisite knowledge to dive into more advanced topics like Applied Machine Learning.
What Our Customers Are Saying:
This course is perfect for...
Ready to Grow
Familiar with Excel
You are comfortable working with spreadsheets.
Early-Career Finance Professional
Finance Student
New to Python
Veteran finance professionals
Career Opportunities
JP Morgan has recently stated and decided that all incoming analysts and associates will learn Python. They already employ over 5400 non technologists that have this skill. Learn how to give your firm what it needs, more than anyone else by acquiring a skill set that can be applied for strong gains in efficiency across many different positions and case studies.
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.
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 analysts leverage Python for data analysis and financial modeling. ML can be applied to predict financial outcomes, evaluate investment opportunities, and perform scenario analysis.
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.
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.
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.
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.
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.
Meet Your Instructor 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.
While CEO, Zach trained hundreds of students and finance professionals to code Python and develop their own Machine Learning algorithms, before stepping down from day to day operations to pursue other endeavours.