Sign in Subscribe

hands on machine learning with scikit learn and tensorflow

Last updated on 


"Hands-On Machine Learning with Scikit-Learn and TensorFlow" is a popular book written by Aurélien Géron. The book provides a practical and hands-on approach to learning machine learning using two widely used Python libraries: Scikit-Learn and TensorFlow. It covers various aspects of machine learning, from the basics to advanced topics, and includes practical examples and exercises.

Here's a brief overview of the key topics covered in the book:

  1. Introduction to Machine Learning:
    • Basic concepts and terminology in machine learning.
    • Overview of different types of machine learning algorithms.
  2. End-to-End Machine Learning Project:
    • A step-by-step guide to building a complete machine learning project.
    • Includes data exploration, preparation, feature engineering, model selection, training, and evaluation.
  3. Classification:
    • Detailed coverage of classification algorithms using Scikit-Learn.
    • Hands-on examples with real-world datasets.
  4. Regression:
    • Regression techniques and algorithms for predicting numerical values.
    • Practical examples with regression problems.
  5. Clustering:
    • Unsupervised learning techniques, including clustering algorithms.
    • Applications and examples of clustering in real-world scenarios.
  6. Dimensionality Reduction:
    • Techniques to reduce the dimensionality of data.
    • Principal Component Analysis (PCA) and other methods.
  7. Model Evaluation and Hyperparameter Tuning:
    • Strategies for evaluating machine learning models.
    • Techniques for fine-tuning model hyperparameters.
  8. Support Vector Machines:
    • In-depth coverage of Support Vector Machines (SVM) for classification and regression.
  9. Decision Trees and Ensemble Learning:
    • Decision tree algorithms and ensemble methods like Random Forests.
  10. Neural Networks and Deep Learning with TensorFlow:
    • Introduction to neural networks and deep learning.
    • Hands-on examples using TensorFlow for building and training neural networks.
  11. Custom Estimators and TensorFlow Extended:
    • Creating custom machine learning models with TensorFlow.
    • Overview of TensorFlow Extended (TFX) for deploying production-ready models.
  12. Natural Language Processing and Recommender Systems:
    • Applications of machine learning in natural language processing.
    • Building recommender systems.