Note: The Coursera courses mentioned in the blog can be audited for free, meaning that you have access to all the course content without any cost. Programming is an essential part of your data science journey. If you know how to code in R, Python, or Julia, it will be quite easy for you to translate algorithms into functions. Moreover, you will learn better techniques to create a program or data reports. I will highly recommend you start with Python and learn the basic syntax and advanced functionalities.

Note: The Coursera courses mentioned in the blog can be audited for free, meaning that you have access to all the course content and can read and view it without any cost. Machine learning is the backbone of modern technology. Almost every big company in the world is trying to use it to get the most out of the data. By taking the free courses, you will learn about classification, regression, clustering, and reinforcement learning. Moreover, you will learn about feature engineering, advanced algorithms, and optimizing techniques.

There are many low-quality free courses and YouTube courses that provide no help in building strong machine learning fundamentals. You will end up even more confused and quit pursuing the career. I am a big advocate of paid courses, but you can also learn a lot from interactive free courses by Udacty, Coursera, and FastAI. These courses cover fundamentals and introduce you to supervised, unsupervised, and deep learning algorithms. You will be introduced to machine learning applications, examples, and building your first linear and logistic regression model on Jupyter Notebook.

The code from the Machine Learning Bookcamp book and a free course based on the book - GitHub - alexeygrigorev/mlbookcamp-code: The code from the Machine Learning Bookcamp book and a free course based on the book

This course will teach machine learning concepts with Tensorflow. In this course, you will explore a large dataset using Datalab and BigQuery, and learn how to use Pandas in Datalab, and sample a dataset for local development. Then you will develop a machine learning model in Tensorflow and operationalize the model. In the end, this course explains how to preprocess data at scale for machine learning and lets you train a machine learning model at scale on the Cloud AI Platform.

Data Science is the buzzword, and a lot of enthusiasts are interested in learning its fundamentals to make a lucrative career in this field. Linear Algebra is one of the important concepts to learn how to perform data transformation techniques like pre-processing, dimensionality reduction, etc. There are many courses available at your fingertip, but it is difficult to choose the right course suited for your requirement. That's precisely the intent of this post - it makes your course search easy by listing down the five free courses to learn linear algebra foundations for data science. Before I go straight into listing down the courses for you, let me first explain the commonly asked questions – why do we need to learn linear algebra in the first place?

Before forwarding the data to data processing and machine learning training, it is helpful to visualize data using the Matplotlib module in Python. It creates graphs and charts using object-oriented APIs and Python GUI toolkits. Additionally, Matplotlib offers a MATLAB-like user interface so that users may perform operations that MATLAB can perform. This open-source, free package offers multiple extension interfaces that connect the matplotlib API to a variety of other libraries.

This Free Udemy course has 3 sections. In the first section, you will learn R basics and how to download R and Rstudio. In the next section, you will learn how to code in R programming and understand functions, loops, R datasets, and R dataframes. The last section teaches how to load CSV files in R, how to apply a family of functions, how to test for normality, KNN classification, LDA(Linear Discriminant Analysis), etc. Overall, this is a good course for beginners to learn R programming basics.

Dask is a powerful open-source Python parallel computing framework. Dask scales Python programs from single-core local workstations to huge distributed cloud clusters. Dask provides a familiar user experience by replicating the APIs of other PyData ecosystem programs like Pandas, Scikit-learn, and NumPy. It also offers low-level APIs that allow programmers to execute bespoke algorithms concurrently.

Are you looking for Best Free Online Data Analytics Courses? If yes, then this article is for you. In this article, you will find the 7 Best Free Online Data Analytics Courses from various platforms. These free data analytics courses will help you to learn data analytics free of cost. All courses are completely free.