by Marco Bonzanini | Released August 2017 | ISBN: 9781788294294

Learn practical solutions to Data Science problems with PythonAbout This VideoEasy to follow guide that will take you from being a beginner to a regular data science task.Get solutions to your common and not-so-common data science problems.Highly Practical, real world examples that make data science your comfort zone.In DetailData Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise.This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies. The audience will learn how to deal with data with a time dimension and how to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). They will learn how to perform text preprocessing steps that are necessary for every text analysis applications. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.The video takes you through with machine learning problems that you may encounter in your everyday use. In the end, the video will cover the time series and recommender system. By the end of the video course, you will become an expert in Data Science Techniques using Python. Show and hide more

1. Chapter 1 : Exploring Your Data
   • The Course Overview 00:08:35
   • Loading Data into Python 00:09:22
   • A New Data Set – Exploratory Analysis 00:11:08
   • Getting Data in the Right Shape – Preprocessing and Cleaning 00:10:18
2. Chapter 2 : Dealing with Text
   • Tokenization – From Documents to Words 00:11:25
   • Stop-Words and Punctuation Removal 00:11:09
   • Text Normalization 00:08:27
   • Calculating Word Frequencies 00:09:07
3. Chapter 3 : Machine Learning Problems
   • Brief Overview of scikit-learn 00:06:22
   • Regression Analysis – Predicting a Quantity 00:09:53
   • Binary Classification – Predicting a Label (Out of Two) 00:14:40
   • Multi-Class Classification - Predicting a Label (Out of Many) 00:04:08
   • Cluster Analysis – Grouping Similar Items 00:07:34
4. Chapter 4 : Time Series and Recommender Systems
   • Time Series Analysis with Pandas 00:11:27
   • Building a Movie Recommendation System 00:19:11
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