Every course from every provider we track, with the same filters over all of them: level, length, price, certificate and who stands behind it.
185 courses for “Machinery Fault Diagnosis and Signal Processing”
2 hours
Two and a half hours on decision trees — how one is built, and how it is read.
4 hours
Three and a half hours on clustering: k-means, hierarchical methods, and choosing between them.
New courses, a couple of emails a month. One click unsubscribes.
10 hours
Ten hours of data mining: the techniques, the algorithms and what each is for.
22 minutes
How categorical columns become numbers a model can use, and what goes wrong when they do not.
5 minutes
Natural Language Processing (NLP)
The shortest useful answer to what NLP is and where it is used.
12 minutes
Natural Language Processing (NLP)
Twelve minutes on what natural language processing is and how it came to work.
8 minutes
What independence means in probability, and what changes when events depend on each other.
17 minutes
Sample spaces, events and the basic rules of probability, in seventeen minutes.
12 hours
Eleven hours of statistics and probability for data science, from first principles.
19 hours
Nineteen hours across SQL, Tableau, Power BI, Python, Excel and pandas — the analyst toolset in one course.
4 hours
Four hours of data analysis in Python: NumPy, pandas, Matplotlib and Seaborn.
10 hours
Generative AI for Data Science
Nine and a half hours explaining generative AI for somebody starting from nothing.
23 hours
Generative AI for Data Science
Twenty-two hours on generative AI — the longest of these, and the most thorough.
2 hours
Two and a quarter hours of R for beginners, taught in Hindi.
3 hours
Eleven lessons on MySQL, from the first SELECT to the joins and aggregates an analyst uses daily.
24 hours
Twenty-four hours of business analytics: the statistics, the tools and the reporting.
1 hour
A live session working one real dataset end to end, from first look to engineered features.
2 hours
Exploratory Data Analysis (EDA)
Two and a quarter hours of exploratory data analysis in Python, taught in Hindi.
3 hours
Exploratory Data Analysis (EDA)
Three hours on looking at a dataset properly before modelling it, and on the features built from what you find.
4 hours
Three and a half hours of OpenCV in Python, from reading an image to detecting faces.
