Key Information
Python Machine Learning Immersive
Python for Data Science Immersive
Data Science Certificate
$1,895 30 Hours
$1,495 30 Hours
$3,495
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Learn More
Learn More
Download Syllabus
Download Syllabus
Download Syllabus
Overview
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.
Pick up Python fundamentals and quickly transition into analyzing real-world datasets. You will learn to how to clean and combine data, as well as generate useful statistics and visualizations. The final sessions will be focused on using linear regression to extrapolate from data and make predictions.
Master the tools to become a data scientist: Python, SQL, automation, and machine learning. Learn Python programming fundamentals and analyze data with Pandas, NumPy, and Matplotlib, and query databases with SQL. Use machine learning to apply regressions and other statistical analysis to create predictive models.
Prerequisite
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
Open to Beginners
Location
29 E Madison, Chicago or Live Online
29 E Madison, Chicago or Live Online
29 E Madison, Chicago or Live Online
Schedule
Not currently scheduled
Not currently scheduled
Not currently scheduled
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
  • 30 HoursPython Machine Learning Immersive
  • 6 HoursPython for Automation
  • 18 HoursSQL Bootcamp
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.
Financing See our Leif FAQ for more details
Target Audience
  • 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
  • Individuals looking to break into data science with Python, machine learning, and SQL skills
  • Analysts who work with other data tools looking to transition to Python and SQL
  • Developers looking to broaden their skillset to data science and Python
What You’ll Learn
  • 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
  • Foundational programming concepts including loops, functions, and objects
  • Handle different types of data, such as integers, floats, and strings
  • Control the flow of your programs with conditional statements
  • Reuse and simplify code with object-oriented programming
  • Analyze tabular data with Numpy and Pandas
  • Create graphs and visualizations with Matplotlib
  • Make predictions with linear regression, using scikit-learn
  • Analyze tabular data with NumPy and Pandas
  • Create graphs and visualizations with Matplotlib
  • Make predictions with linear regression 
  • Applying Machine learning algorithms to the data
  • Cleaning and balancing data in Pandas
  • Evaluating the performance of machine learning models  
  • Combine information across tables with join statements
  • Advanced techniques such as subqueries and stored procedures 
  • Learn how to write programs in Python to automate everyday tasks