- Expert Mentors
- Expert Mentors
- Learning Community
- Live Classes
- Top Freelance Developers
- How it Works
- Become a Codementor
Ben spent his past academic life on studying character recognition in deep learning lab,
and studying how to automate diagnosis of certain neurodegenerative diseases.
Nowadays, he spends his days working as a data scientist, and his nights exploring data he finds online,
learning new programming languages, and brewing beer.
Pete had 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.
This is a class for anyone who wants to get a foundation in python, machine learning and data science. We’re going to learn how to express ideas in python, use popular machine learning frameworks, acquire, maintain, and clean data, and interpret machine learning predictions. After knowing a basic knowledge of Python, you will learn how to apply machine-learning to real-world (commercial) problems. We concentrate on practical application, covering theory only where necessary.
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 comercially viable predictive question; apply machine-learning algorithms to create a predictive engine; and evaluate the effectiveness of that engine. We will explore several supervised and unsupervised learning methods.
In this lesson, we’ll take a dive into the python programming language. We’ll talk about basic data types (lists, strings, integers), and data flow mechanisms (for loops, if statements). By the end of this lesson, we’ll be able to start solving basic problems with code!
In this lesson, we’ll explore some more advanced python features such as dictionaries, functions, and files. We’ll also start to talk about what machine learning is, and some fundamental concepts related to data analysis.
In this lesson, we’ll learn about some specific machine learning models, and use Scikit Learn to build, train, and evaluate them on a real world dataset.
In this lesson, we’ll continue focusing on training and improving machine learning models, and also chat about how to visualize, explain, and evaluate our models.
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.
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.
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.
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.
Need to pause and think through a concept before moving forward? Go through the classes at your own pace and learn the core concept of Python!
Get practical coding experience through hands-on projects.
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.
For only $99, you’ll get unlimited access to prerecorded Python courses and Machine Learning courses for beginners. This includes 4 prerecorded Beginner Python lessons and 4 prerecorded Machine Learning lessons, in total 16 hours of Python and ML courses with hands-on exercises. Enroll in this class and schedule your own class time.
You will have access to 8 prerecorded courses, which will ALL be available once you enroll. Each prerecorded course 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!
The prerecorded classes will be released all at once upon enrollment and will be available indefinitely. You can schedule your time the way you like and even go back to review ALL 8 lessons any time!
This is for people how are interested in getting into Machine Learning and Data Science. No prior Python programming experience necessary, but prior knowledge of programming will be useful.