Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data
Authors: Rohan Chopra – Aaron England – Mohamed Noordeen Alaudeen
ISBN-10: 1838552863
ISBN-13: 9781838552862
Publication Date 出版日期: 2019-07-19
Print Length 页数: 426 pages
Book Description
By finelybook
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.
Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning,supervised learning,and reinforcement learning. You will also explore basic classification and regression techniques,such as support vector machines,decision trees,and logistic regression.
As you make your way through chapters,you will study the basic functions,data structures,and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation,study how to use Matplotlib to create highly customizable visualizations,and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters,you will explore convolutional neural networks (CNNs),deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network,make the model process contextual information,and create human language processing systems to predict the outcome.
By the end of this book,you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
What you will learn
Pre-process data to make it ready to use for machine learning
Create data visualizations with Matplotlib
Use scikit-learn to perform dimension reduction using principal component analysis (PCA)
Solve classification and regression problems
Get predictions using the XGBoost library
Process images and create machine learning models to decode them
Process human language for prediction and classification
Use TensorBoard to monitor training metrics in real time
Find the best hyperparameters for your model with AutoML
