.png){kind=logo}
Welcome to the guide on free data science courses on YouTube for 2024. You'll learn about a range of free data science courses on YouTube with the help of this in-depth instruction. These classes are meant to provide you with the information and abilities needed in the always-changing field of data science.
These Free data science courses on YouTube are a great way to improve your knowledge and skills in Data Science, regardless of your level of expertise. Let's start this fascinating educational adventure by exploring the world of YouTube data science courses!
The basis for R programming and data analysis is provided in the Harvard University course Data Science: R Basics. It goes over fundamental R programming ideas, basic R syntax, and utilizing dplyr to do tasks like sorting and data wrangling. Making charts for data visualization is another aspect of the training.
A thorough introduction to data analysis using Python is offered via IBM's "Master Python for Data Analysis" course. Both fundamental and complex subjects are covered, such as statistical analysis, data visualization, and forecasting future trends. The training provides hands-on experience through practical projects.
The extensive curriculum of Stanford University's Machine Learning course on Coursera includes both the theory and application of machine learning. It covers subjects including supervised learning, unsupervised learning, and best practices in AI and machine learning and is taught by AI innovator Andrew Ng.
Deep learning theory and practice are covered in detail in the "Deep Learning" course offered by deeplearning.ai on Coursera, taught by AI pioneer Andrew Ng. Using TensorFlow and Python, it covers subjects including generative adversarial networks, recurrent neural networks, and convolutional neural networks.
An intermediate-level course, "Natural Language Processing," is offered by the National Research University Higher School of Economics on Coursera. Natural language processing theory and practice are covered, including topic modeling, sentiment analysis, text categorization, text preprocessing, and machine translation.
Comprehensive training in computer vision may be obtained through the University of Buffalo's "Computer Vision Basics" course on Coursera. Key topics covered include artificial intelligence, digital signal processing, and neurobiology. Additionally, for practical experience, the course includes projects.
A thorough introduction to the Big Data environment may be found in the University of California, San Diego's "Introduction to Big Data" course on Coursera. In addition to introducing the Hadoop framework, which has made big data analysis more approachable, it covers important ideas, applications, and systems.
The fundamentals of data storytelling using Tableau may be obtained through the University of California, Davis's "Data Visualization with Tableau" course, which is available on Coursera. It covers setting up eye-catching dashboards and graphics, selecting appropriate KPIs to track, and bringing audiences together around a single objective.
The "Statistics and Probability" course at Khan Academy provides an extensive curriculum that covers everything from advanced regression and analysis of variance to the analysis of categorical data. It offers a strong foundation in probability and statistics with engaging examples and learning activities.
The comprehensive course "Mathematics for Machine Learning" offered by Imperial College London on Coursera helps to close the gap between machine learning and mathematics. It covers dimensionality reduction with principal component analysis, multivariate calculus, and linear algebra, giving advanced machine learning courses the necessary mathematical background.
Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp
_____________
Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.
