Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

In this 10-hour course, you'll master the practical applications of machine learning techniques using TensorFlow 2.0 and Scikit-Learn. Discover how to solve real-world problems with models handling diverse data through implementation-driven lessons. By focusing on hands-on projects, this course ensures you gain practical, actionable skills.

What I will be able to do after this course

• Understand the core concepts of machine learning and its applications using Scikit-Learn.
• Develop and optimize supervised and unsupervised learning algorithms.
• Explore and build deep learning models for tasks like image and text processing using TensorFlow 2.0.
• Become adept at working with structured and unstructured data in machine learning workflows.
• Learn best practices for deploying machine learning models in production environments.

Course Instructor(s)

Samuel Holt is an experienced data scientist and instructor with a strong background in machine learning and deep learning. With years of practical experience applying TensorFlow and Scikit-Learn to solve industry problems, Samuel is also passionate about teaching and breaking down complex concepts into understandable lessons. He is dedicated to helping learners build confidence and mastery in practical machine learning.

Who is it for?

This course is designed for developers and data professionals familiar with Python, Pandas, and NumPy who are looking to enhance their machine learning skills. It's ideal for individuals aiming to apply ML in real-world projects, tackling challenges such as automation, data prediction, and problem inference. Learners seeking to deepen their technical expertise in TensorFlow and Scikit-Learn will find this course invaluable.