Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data

Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data
Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data by Rohan Chopra
English | 2019 | ISBN: 1838552862 | 426 Pages | EPUB | 101 MB

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