Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
117 courses for “Scikit-Learn Tutorial — Machine Learning in Python”
12 hours
Twelve hours of Python for beginners, taken slowly enough to follow along.
4 hours
Four and a half hours of Python from nothing, the course most people start with.
New courses, a couple of emails a month. One click unsubscribes.
55 minutes
Confusion matrices, precision, recall and ROC curves, worked through in scikit-learn.
45 minutes
Forty-five minutes of scikit-learn, from fitting a model to scoring it.
13 minutes
Thirteen minutes on how a model is judged, and why accuracy alone is rarely the answer.
2 hours
An hour and a half of forecasting in Python, for somebody who has not done it before.
13 minutes
What machine learning is, explained without the mathematics, in thirteen minutes.
19 hours
Nineteen hours across SQL, Tableau, Power BI, Python, Excel and pandas — the analyst toolset in one course.
5 hours
Five hours on analysing data in Python, from loading a file to a finished chart.
4 hours
Four hours of data analysis in Python: NumPy, pandas, Matplotlib and Seaborn.
9 hours
Nine hours of React, taught step by step.
5 hours
Five hours of React for somebody who has never written any.
2 hours
Two hours of R, taught as what it is: a language built for statistics.
2 hours
Exploratory Data Analysis (EDA)
Two and a quarter hours of exploratory data analysis in Python, taught in Hindi.
4 hours
Three and a half hours of OpenCV in Python, from reading an image to detecting faces.
7 hours
Seven hours building neural networks in TensorFlow 2, for people who have not built one before.
57 minutes
The opening lecture of MIT's introductory deep learning course.
9 hours
Eight and a half hours on deep learning, from neural networks to the frameworks that train them.
10 hours
Ten hours of deep learning from Edureka, from the neural network up.
7 hours
A seven-hour tutorial on probability and statistics for data science, from a Stanford PhD.
