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Class time and plan

“I'll show you how to use machine-learning to tackle real-world predictive analyses. We'll start with industrial-strength data sets. We'll break them down and understand them. We'll see what commercially valuable models we can extract.”

Peter Figliozzi, Ph. D.

Pete is a professional data scientist. He has used machine-learning algorithms to create predictive engines in variety of fields including marketing, mechanical prognostics and health management, and algorithmic stock trading. Pete enjoys Codementoring and the great interaction and learning that comes with it. Pete's degree is in Physics from the University of Texas, Austin.

Codementor Online Machine Learning Class - Schedule Your Own Classes!

Who is this class for?

This class is for anyone who wants to learn how to apply machine-learning to real-world (commercial) problems. All you'll need is a basic, working knowledge of Python. No machine-learning experience is required.

What will you learn?

By the end of the class, you'll be able to: analyze a real-world, industrial-strength data set; explore that data set; formulate a commercially viable predictive question; and apply machine-learning algorithms to create a predictive engine.

Codementor Machine Learning Class: Syllabus

Lesson 1

College Degrees

We'll use the U.S. Department of Education College Scorecard dataset to build a college recommendation service. 

This introductory lesson will cover the essentials: setting up and working with Jupyter notebooks; importing, cleaning, and assembling real-world data with the Pandas library; displaying inline graphs and charts with Pandas and matplotlib. 

We'll use Pandas together with Sci-kit Learn's preprocessing and clustering modules to characterize the data.


Lesson 2

College Degrees Part II

We'll use our findings from Lesson 1 to construct the college recommendation service. 

We'll learn and apply Sci-kit Learn's principal component analysis (PCA), together with a suitable clustering algorithm. 

Once we finish, we'll show how to take code out of the Jupyter notebook, respond to JSON-formatted queries, and provide a response.

Lesson 3

Public Opinion

Using a full month of Reddit comments, we'll use some natural language processing (NLP) techniques to infer public opinion on different types of cars. 

We'll discuss the Python NLP landscape, using the Natural Language Toolkit (nltk) for sentiment analysis, text pre-processing, and descriptive statistics. 

We will construct sparse-vector (bag-of-word) models, apply term-frequency inverse document-frequency TF-IDF weightings, and retrieve similar documents using cosine-similarity. 

We will also construct more advanced models including Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI), and paragraph vectors/doc2vec using the gensim library. 

We will demonstrate LDA visualization and other document visualization methods.

Lesson 4


We'll use data from an online lender to build a prediction engine for default/late-payment risk. 

We will use Sci-kit learn to evaluate our input variables for predictive power and build a random-forest classifier. We'll learn how to visualize our model using graphViz and evaluate its effectiveness using receiver-operator characteristic (ROC) curves.

Bonus: Online advertising-- we'll explore Bayesian statistical models and A/B testing.

You Will Get

Learn ML at Your Pace

Need to pause and think through a concept before moving forward? Go through the classes at your own pace and learn to apply Machine Learning to your life!

Hands-on Exercises

Get practical coding experience through hands-on projects.

Unlimited Lesson Playbacks

Didn't get the concept fully the first time? You can watch and review all prerecorded classes as many time as you'd like, whenever you'd like.

Frequently Asked Questions

How is this different from other courses?

For only $69, you’ll get to learn Python for Machine Learning with your own schedule. This includes 8 hours of prercorded classes with hands-on exercises to test your learning. Enroll to this class and schedule your own class time. Make your learning much easier by doing it at your pace.

What is the class structure?

You will have access to 4 prerecorded classes, which will be available at once upon your enrollment. Each prerecorded class is around 2 hour long. You will get to code along the recordings at your own pace and able to check back on them whenever you like!

What time are the classes?

The prerecorded classes will be released upon your enrollment and will be available indefinitely. You can schedule your time the way you like and even go back to review ALL 4 classes any time!

What do I need to know before the class?

This class is for anyone who wants to learn how to apply machine-learning to real-world (commercial) problems. All you'll need is a basic, working knowledge of Python. No machine-learning experience is required.

Class Enrollment Starts Now

This is a intermediate Python prerecorded class for Machine Learning.
The videos will be available upon sign up.

Questions? We're here to help!

Have questions about this live class or Codementor in general? Our team is here to help!

Class time and plan