Language: English Year: 2016 Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Some of the most remarkable of these properties are its easy to read code, suppression of non-mandatory delimiters, dynamic typing, and dynamic memory usage. The text is released under the CC-BY-NC-ND license, … File size: 20.3 MB A lot of description about privacy and ethics Why are you waiting for? Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Recipes also include a discussion that explains the solution and provides meaningful context. How do you make informative figures that are visually pleasing? Matplotlib: includes capabilities for a flexible range of data visualizations in Python. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. This is a book about doing data science with Python, which immediately begs the question: what is data science? With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills, Exam Prep for Python Data Science Handbook, Introduction to Machine Learning with Python, Shackelford’s Surgery of the Alimentary Tract, Contemporary Digital Forensic Investigations of Cloud and Mobile Applications, Environmental Impact of Mining and Mineral Processing, Cell Instructive Materials to control and guide cell function, Polymer Electrolyte Fuel Cell Degradation, Nanotechnology Applications for Cancer Chemotherapy pdf, intercultural communication pdf book free. With a breakdown of data science, data analysis, and even the wonders of machine learning, this collection is perfect for anybody looking to begin their programming journey. Here it is highly recommended to the readers that they search for other online resources to cla, Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter Notebook running Python Use Matplotlib to create data visualizations Fit a model using scikit-learn Use lasso and ridge regression to reduce overfitting Fit and tune a random forest model and compare performance with logistic regression Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques, This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your da, Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. From there, you can insert, combine, or adapt the code to help construct your application. With all the data available today, machine learning applications are limited only by your imagination. Click Get Books and find your favorite books in the online library. Data files and related material are available on GitHub. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. So don't wait! The more you practice the more you will learn. So wha, A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person.