R Language for Beginners (Hindi)
2 hours
Two and a quarter hours of R for beginners, taught in Hindi.
Technology is the widest part of this catalogue, and the place most people start. Underneath it sit cloud computing, artificial intelligence, cybersecurity, data science, web development, DevOps, databases, game development, UX/UI design, product management and more, each with its own page and its own filters.
Most of what is listed here is pitched at beginners, and a large share costs nothing to begin. Courses come from YouTube creators working in the open alongside structured programmes from Noble Desktop and the UC San Diego Division of Extended Studies, so a first hour of curiosity and a paid certificate track sit side by side rather than in separate worlds.
Every course is compared on the same terms: level, length, price, whether it ends in a certificate, and who stands behind it. Use the filters to narrow by subject, by level or by length, and open any course to see where it is taught before you commit an afternoon to it.
220 courses
2 hours
Two and a quarter hours of R for beginners, taught in Hindi.
5 hours
Five hours of SQL for analytics in one sitting.
3 hours
Eleven lessons on MySQL, from the first SELECT to the joins and aggregates an analyst uses daily.
13 hours
Sixteen lessons on turning raw columns into the features a model can use.
10 hours
Ten hours of business analytics, from the statistics through to the dashboards.
24 hours
Twenty-four hours on the business analyst role itself — the process, not only the tooling.
10 hours
Ten hours of business analysis from the beginning, for somebody with no background in it.
24 hours
Twenty-four hours of business analytics: the statistics, the tools and the reporting.
12 hours
Eleven hours on the big data stack, from what the term means to the tools that do the work.
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.
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.
1 hour
An hour on descriptive statistics: the measures that summarise a dataset before any model touches it.
7 hours
A seven-hour tutorial on probability and statistics for data science, from a Stanford PhD.
7 hours
Twenty-seven lessons on statistics for data analysis and data science, taught in Hindi.
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