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.
Key Features
- Explore the depths of data science, from data collection through to visualization
- Learn pandas, scikit-learn, and Matplotlib in detail
- Study various data science algorithms using real-world datasets
Book Description
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
Who this book is for
Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
Table of Contents
- Preface
- Introduction to Data Science and Data Preprocessing
- Data Visualization
- Introduction to Machine Learning via Scikit-Learn
- Dimensionality Reduction and Unsupervised Learning
- Mastering Structured Data
- Decoding Images
- Processing Human Language
- Tips and Tricks of the Trade
Rohan Chopra graduated from Vellore Institute of Technology with a bachelor's degree in computer science. Rohan has an experience of more than 2 years in designing, implementing, and optimizing end-to-end deep neural network systems. His research is centered around the use of deep learning to solve computer vision-related problems and has hands-on experience working on self-driving cars. He is a data scientist at Absolutdata.
Aaron England earned a Ph.D from the University of Utah in Exercise and Sports Science with a cognate in Biostatistics. Currently, he resides in Scottsdale, Arizona where he works as a data scientist at Natural Partners Fullscript.
Mohamed Noordeen Alaudeen is a lead data scientist at Logitech.
Noordeen has 7+ years of experience in building and developing end-to-end BigData and Deep Neural Network Systems. It all started when he decided to engage the rest of his life for data science.
He is Seasonal data science and big data trainer with both Imarticus Learning and Great Learning, which are two of the renowned data science institutes in India. Apart from his teaching, he does contribute his work to open-source. He has over 90+ repositories on GitHub, which have open-sourced his technical work and data science material. He is an active influencer( with over 22,000+ connections) on Linkedin, helping the data science community.
