| Sumario: | Learn Python programming and Scikit-Learn applied to machine learning regression in this comprehensive guide for beginners About This Video Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence Build artificial neural networks with TensorFlow and Keras Make predictions using linear regression, polynomial regression, and multivariate regression In Detail Machine learning is a branch of computer science in which you can use mathematical input to develop complicated models that fulfil various roles. Python is a popular choice for building machine learning models because of the large number of libraries available. This course will walk you through an astonishing combination of Python and machine learning, teaching you the fundamentals of machine learning so you can construct your own projects. We will begin by studying Python programming and applying Scikit-Learn to machine learning regression in this course. After that, we will look at the theory underpinning simple and multiple linear regression algorithms. Following that, we will look at how to solve linear and logistic regression issues. Later, we will use sklearn to learn both the theory and the actual application of logistic regression. We will also go into the math underpinning decision trees. Finally, you will learn about the various clustering algorithms. By the end of this course, you will be able to use these algorithms in the real world. Audience This course is for anyone interested in pursuing a career in machine learning, as well as Python programmers who want to add machine learning skills to their resume. This course will also benefit technologists who want to learn more about how machine learning works in the real world. This course requires familiarity with the fundamentals of Python, as well as readiness, flexibility, a will to learn, and, most importantly, basic mathematical skills.
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