Key Information
Machine Learning & Automation for Finance
Python Machine Learning Immersive
FinTech Bootcamp
$1,295 18 Hours
$1,895 30 Hours
$4,495 102 Hours
Overview
This course will begin with advanced Python and statistic topics such as object-orientated programming and regression models. Students will learn how to apply these concepts using real-world financial data by building a predictive returns model using regression. We will then introduce students to important financial statements and ratios, and how to pull data from these statements and compute these important financial ratios using Python.
Take a step beyond normal programming, into using algorithms that can independently learn patterns and make decisions. Machine learning skills are in high demand, as these algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.
Learn Python for financial analysis, machine learning, and SQL from experienced finance & engineering professionals in this 102-hour immersive.
Prerequisite
Open to Beginners
This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Immersive before taking this course. 
Open to Beginners
Location
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
Schedule
Next start date: October 25–26, Monday to Tuesday, 10–5pm
Other scheduling options Weekdays only
View full schedule
Next start date: October 23–November 20, Saturdays, 10–5pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: Oct 11–Nov 3, Weekdays
Other scheduling options: Weekdays only
View full schedule
Certification
Receive a Certificate of Completion
Receive a Certificate of Completion
New York-licensed Certificate Program
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Courses Included (Certificates & packages only)
N/A
N/A
  • 30 HoursPython for Data Science Immersive
  • 18 HoursSQL Bootcamp
  • 6 HoursPython for Automation
  • 30 HoursPython Machine Learning Immersive
  • 18 HoursPython for Finance Immersive
Discounts See our discounts policies for more details
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • This is a discounted package of classes that is 15–25% off the individual class prices. Other discounts do not apply.
Payment Plan See our payment plan FAQ for more details
This program is eligible for our “pay-as-you-go” payment plan.
12-Month FinancingSee our Climb Credit FAQ for more details
This program is eligible for financing through Climb Credit.
Target Audience
Anyone
  • Confident python developers who would like to explore machine learning hands-on
  • Developers with strong skills in another language, and some background working with data looking to building machine learning models
Anyone
What You’ll Learn
See course details for more info
  • How to clean and balance your data using the Pandas library
  • Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
  • Choosing good features to use as input for your algorithms
  • Properly splitting data into training, test and cross-validation sets
  • Important theoretical concepts like overfitting, variance and bias
  • Evaluating the performance of your machine learning models
  • Python: Handling different types of data, such as integers, floats, and strings
  • Python: Analyze tabular data with Numpy and Pandas and graph the results using Matplotlib
  • Python: Learn how to pull data from multiple sources 
  • Machine Learning: Learn how to analyze a financial statement using Python 
  • Machine Learning: Learn financial topics such as WACC, NPV, IRR
  • Machine Learning: Learn how to build predictive models for internal budgeting
  • SQL: Explore and alter data using a graphical user interface
  • SQL: Write queries to search through tables programmatically
  • SQL: Understand different data types and converting between them
  • SQL: Combine information across tables with join statements
  • SQL: Advanced techniques like subqueries and timestamp functions