Natural Language Processing in 5 Minutes
5 minutes
Natural Language Processing (NLP)
The shortest useful answer to what NLP is and where it is used.
Data science here means the whole path from a raw table to something somebody can act on: cleaning and shaping data, the statistics that stop you fooling yourself, and the charts that carry a finding to people who were not in the room. Data visualisation has a page of its own beneath this one, covering the individual tools in depth.
Everything listed under data science is free to start, and most of it is pitched at beginners, with a smaller set of intermediate and advanced courses for people who already work with data and want a specific gap filled. The courses come from YouTube, where the teaching is done in public and you can judge an instructor before spending an evening with them.
Each course is compared on the same terms: level, length, price, certificate and the source it comes from. Filter by level to skip the introductions if you have already done them.
120 courses
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.
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.
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.
12 hours
Generative AI for Data Science
Twelve hours across the generative AI stack, from the models to what is built with them.
2 hours
Two hours of R, taught as what it is: a language built for statistics.
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.
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