Python Machine Learning
A Beginner’s Guide to Python Programming for Machine Learning
Learn the essential tools every beginner should know about Python. Get the methods that will help you complete your projects successfully like the pros. This is the book every aspiring programmer needs to have. Learn how to try fresh ideas and learn problem-solving, improve your programming skills, but above all, boost your confidence. Imagination and creativity will open the door to new projects you never thought possible.
Here’s what you will love about this book:
- What is Python Machine Learning, anyway? Here’s how to get started.
- Find out the “Whys” and “Hows” of Python
- The One Proven Way for Effective Implementation of Machine Learning Algorithms
- Find Out the EASIEST Way for Mastering Machine Learning with Python.
- Learn Importance of Learning Data Analysis in Python.
- The truth about Deep Learning vs Machine Learning
- The Secret to Machine Learning with Scikit-Learn
- Discover Deep Learning with TensorFlow.
- The Essential Key Tips & Tricks for Deep Learning with PyTorch and Keras.
- Find out The Role of Machine Learning in the Internet of Things (IoT)
- Looking to the Future with Machine Learning. The Business Angle.
- A beginners’ friendly book with easy-to-follow tips.
- And much more, this is truly a must-have guide!
Introduction Chapter 1: Machine Learning: A Brief History Donald Hebb - The Organization of Behavior Samuel Arthur - Neural Networks, Checkers and Rote Learning Rosenblatt’s Perceptron Marcello Pelillo - The Nearest Neighbor Algorithm Perceptrons and Multilayers Going Separate Ways Robert Schapire - The Strength of Weak Learnability Advancing into Speech and Facial Recognition Present Day Machine Learning Chapter 2: Fundamentals of Python for Machine Learning What is Python? Why Python? Other Programming Languages Effective Implementation of Machine Learning Algorithms Mastering Machine Learning with Python Chapter 3: Data Analysis in Python Importance of Learning Data Analysis in Python Building Predictive Models in Python Python Data Structures Python Libraries for Data Analysis Chapter 4: Comparing Deep Learning and Machine Learning Deep Learning vs Machine Learning Problem Solving Approaches Different Use Cases Chapter 5: Machine Learning with Scikit-Learn Representing Data in Scikit-Learn Features Matrix Target Arrays Estimator API Supervised Learning in Scikit-Learn Unsupervised Learning in Scikit-Learn Chapter 6: Deep Learning with TensorFlow Brief History of TensorFlow The TensorFlow Platform TensorFlow Environments TensorFlow Components Algorithm Support Creating TensorFlow Pipelines Chapter 7: Deep Learning with PyTorch and Keras PyTorch Model Structures Initializing PyTorch Model Parameters Principles Supporting Keras Getting Started Keras Preferences Keras Functional API Chapter 8: Role of Machine Learning in the Internet of Things (IoT) Fusing Machine Learning and IoT Machine Learning Challenges in IoT Chapter 9: Looking to the Future with Machine Learning The Business Angle AI in the Future Conclusion
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