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Python Machine Learning by Example

Python Machine Learning by Example

By : Yuxi (Hayden) Liu
4 (20)
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Python Machine Learning by Example

Python Machine Learning by Example

4 (20)
By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Index

Training a logistic regression model

Now, the question is how we can obtain the optimal w such that J(w) is minimized. We can do so using gradient descent.

Training a logistic regression model using gradient descent

Gradient descent (also called steepest descent) is a procedure of minimizing an objective function by first-order iterative optimization. In each iteration, it moves a step that is proportional to the negative derivative of the objective function at the current point. This means the to-be-optimal point iteratively moves downhill toward the minimal value of the objective function. The proportion we just mentioned is called the learning rate, or step size. It can be summarized in a mathematical equation as follows:

Here, the left w is the weight vector after a learning step, and the right w is the one before moving, η is the learning rate, and ∆w is the first...

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