Free Data Science Courses: How To Learn Data Science From Youtube?
Free YouTube classes for people who want to become data scientists: Start learning in 2024
Hello and welcome to this help on how to find free data science courses on YouTube in 2024. With this detailed guide, you’ll find out about many free data science classes on YouTube.
The goal of these classes is to give you the knowledge and skills you’ll need in the area of data science, which is always changing.
No matter how much you know about Data Science, these free classes on YouTube are a great way to learn more and get better at it. Let’s start this exciting journey of learning by checking out some data science classes on YouTube!
1. Data Science: How To Get Started With R
This study at Harvard University, called Data Science: R Basics, teaches you the basics of R programming and data analysis. It talks about basic R programming ideas, basic R code, and how to use dplyr to do things like sorting and moving data around. A different part of the training is making charts to show facts.
2. Learn Python For Data Analysis With A Course From IBM
IBM’s “Master Python for Data Analysis” course gives you a complete start to using Python for data analysis. It goes over both simple and complicated topics, like statistical analysis, data display, and predicting what trends will happen in the future. Through practical projects, the training gives people real-world experience.
3. Learning By Machine
In its in-depth lessons, Stanford University’s Machine Learning course on Coursera covers both the theory and practice of machine learning. Andrew Ng, a pioneer in AI, teaches it. It covers things like supervised learning, uncontrolled learning, and best practices in AI and machine learning.
4. Learning In Depth
The “Deep Learning” course on Coursera from deeplearning.ai is taught by AI pioneer Andrew Ng and goes into great depth about both the theory and practice of deep learning. It talks about things like generative adversarial networks, recurrent neural networks, and convolutional neural networks using TensorFlow and Python.
5. Working With Natural Language
From the National Research University Higher School of Economics comes the course “Natural Language Processing,” which is at the intermediate level and is available on Coursera.
Topic modeling, mood analysis, text categorization, text preprocessing, and machine translation are some of the natural language processing techniques and methods that are talked about.
6. Seeing With Computers
The “Computer Vision Basics” course from the University of Buffalo on Coursera can teach you everything you need to know about computer vision.
Artificial intelligence, digital signal processing, and neurobiology are some of the main themes that are covered. The course also includes projects that give students real-world experience.
7. A Lot Of Data
The “Introduction to Big Data” course from the University of California, San Diego on Coursera gives a complete look at the Big Data world. It talks about important ideas, applications, and systems, and it includes the Hadoop framework, which has made big data analysis easier to do.
8. Making Sense Of Data With Tableau
The “Data Visualization with Tableau” course from the University of California, Davis, which can be found on Coursera, will teach you the basics of using Tableau to tell stories with data.
It talks about making dashboards and graphics that stand out, picking the right KPIs to track, and getting everyone to work together toward a single goal.
9. Chances And Statistics
Khan Academy’s “Statistics and Probability” course covers a lot of ground. It goes over advanced regression, analysis of variance, and the study of categorical data, among other things.
It gives you a solid background in probability and statistics by using interesting examples and tasks to help you learn.
10. Math To Help Machines Learn
It is possible to learn more about machine learning and math together with Imperial College London’s “Mathematics for Machine Learning” course on Coursera.
It goes over dimensionality reduction with principal component analysis, multivariate calculus, and linear algebra. This gives students taking advanced machine learning classes the math they need.
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