Python Machine Learning Bundle

Learn the most fundamental Python functions used in the finance industry, and to build investor classifiers using machine learning algorithms.

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Machine Learning Process

Bundle overview

Welcome to CFI's Applied Machine Learning Algorithms Course. The world of finance is changing and now is the time to invest. The skillsets of investment bankers, asset managers, sales and trading professionals are all rapidly evolving. Developing a skillset in machine learning is essential if you want to progress.

Match the Complexity of your Model to the Real Relationship


CFI has partnered with Machine Learning Edge to bring to you the Python Machine Learning bundle. First, in our Python Fundamentals course, you will learn the foundations of Python, used specifically for finance and Excel. Then, in our Applied Machine Learning Algorithms, you’ll utilize the knowledge from Python Fundamentals to build machine learning investor classifiers.

We will explore real case studies from investing banking and capital markets applications being used today to advise Fortune 500 companies all over the world.

Confusion Matrix

By the end of the bundle, you will be able to:

Python Fundamentals

  • Write and execute basic Python code to perform advanced calculation, generate outputs, create variables, abstract from data, etc.
  • Create and manipulate important data structures such as lists, tuples, sets, and dictionaries
  • Use for loops and conditional logic to create your own custom functions
  • Import external packages including NumPy and Pandas
  • Generate random integers and samples
  • Filter, segment, and summarize data
Business Case Overview

Applied Machine Learning Algorithms

  • Identify overfit regression models
  • Compare different regularized regression algorithms and decision tree ensemble algorithms
  • Explain the confusion matrix and its relation to the ROC curve
  • Construct training data sets, testing data sets, and model pipelines
  • Perform advanced data cleaning, exploration, and visualization
  • Engineer features based on conditional relationships between existing features
  • Build and finalize a machine learning classifier
Five Regression Models
Match the Complexity of your Model to the Real Relationship
Confusion Matrix
Business Case Overview
Five Regression Models

Frequently Asked Questions

Can I download the Excel files and financial modeling templates?
Yes! All courses come with downloadable files. In total, you can download more than 12 different financial modeling templates to use.
How long do I have to complete the courses?
You have an unlimited amount of time – the Full bundle is good for two years and you can always rewatch the lessons as many times as you like.
Are CFI courses verified?
Yes! Our courses and certificates count as verified Continuing Professional Education for CPA charterholders. We can also verify certificates with employers if you request it.
Are there any additional fees or costs?
No! The bundle price includes everything and there are no additional fees or charges to earn your digital certificates.
What currency are your courses priced in?
All our online courses are priced in USD.
How do I earn my certificates?
You have to complete all the video lessons, quizzes and final assessments in each course. You can retake the assessments as many times as necessary until you reach an 80% passing grade.
Can I email the instructor if I have questions?
While the courses are self-study, you can ask questions related to technical difficulties or ask content questions on our quarterly webinars.
Will these courses help me advance my career?
Based on hundreds of reviews from thousands of students we know how CFI courses have helped so many people advance their finance careers. Our courses are designed to be extremely practical and simulate the experience of being trained as a professional financial analyst… the ultimate way to advance your career.
More Questions?
Please Email us and we'll be happy to answer any of your questions!

Learn new skills and differentiate yourself in the workplace.

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