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.
You’ll begin this course by studying Python programming and applying Scikit-Learn to machine learning regression. This lays the groundwork for understanding the theory underpinning simple and multiple linear regression algorithms. Following that, you’ll learn how to solve linear and logistic regression issues. The courses further guides you to harness the power of sklearn, grasping the theory and practical application of logistic regression, and then advances to cover the math underpinning decision trees. Finally, you’ll learn about the various clustering algorithms.
By the end of this course, you will be able to use these machine learning algorithms in the real world.
publication date
2022-09-26
what you will learn
Learn the fundamentals of the deep learning theory
Explore classification algorithms for K-Nearest Neighbors, decision tree, and logistic regression
Learn to implement ANN and CNN in Python
Understand the gradient descent algorithm
Explore the different types of activation functions
Explore neural network architecture
key features
Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence (AI) * Build artificial neural networks with TensorFlow and Keras * Make predictions using linear regression, polynomial regression, and multivariate regression
approach
This course is a balance of theory and practical demonstrations in which we will start with the fundamentals and work our way up to implementation. We will be utilizing Python 3.9.7 and Juypter Notebook 6.4.5 in this course.
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.
short description
The purpose of this course is to teach you how to use Python for machine learning to create real-world algorithms. You will gain an in-depth understanding of the fundamentals of deep learning. This course will help you explore different frameworks in Python to solve real-world problems using the core concepts of deep learning and artificial intelligence.
subtitle
Learn to create machine learning algorithms in Python for students and professionals
keywords
Python, machine learning, regression, clustering, classification, algorithm, scikit-learn, mathematics, Simple Linear Regression, CNN, ANN, Gradient Descent, Multiple Linear Regression, K-Nearest Neighbors, Decision Tree, Logistic regression
Product ISBN
9781804619308
