Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn
Build effective models in scikit-learn with TensorFlow 2.0
Created by Samuel Holt
Gain hands-on experience building machine learning models using scikit-learn and TensorFlow 2.0. Learn to solve real-world problems by training, optimizing, and deploying models that make accurate predictions and decisions from data. Develop practical skills that go beyond theory and apply directly to industry tasks.
Packt | Jun 2020 | 628 min
What You Will Learn
You will learn by working through step-by-step exercises and practical examples that show how to apply machine learning methods to real data. Each topic is introduced with clear explanations, followed by hands-on coding in Jupyter notebooks. Tips and best practices are shared to help you get the most from each model and technique.
Key Features
- Build and optimize machine learning models for real-world data challenges
- Apply supervised and unsupervised learning using scikit-learn and TensorFlow 2.0
- Work with unstructured data, images, and text to create effective predictions
Target Audience
Designed for developers with a solid grasp of Python, pandas, and NumPy who want to expand their machine learning skills. If you are looking to apply machine learning to real projects using scikit-learn and TensorFlow 2.0, and want to move from theory to practical implementation, you will find this course a great fit.
