Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, etc

Learn the core concepts and practical skills you need for any machine learning project. You'll explore essential topics such as:
An introduction to machine learning, covering different types of systems like supervised, unsupervised, and reinforcement learning.
A step-by-step guide to a complete machine learning workflow, including data gathering, cleaning, visualization, and model deployment.
Detailed explanations of key algorithms like
Linear Regression, Support Vector Machines (SVMs), and Decision Trees.
How to build powerful
Ensemble models using techniques like Random Forests, AdaBoost, and Gradient Boosting.
Critical concepts such as
data quality, feature selection, and managing overfitting and underfitting to ensure your models perform reliably.