Python Scikit-Learn for Beginners: Scikit-Learn Specialization for Data Scientist - Python for Beginners in Data Science and Data Analysis

Python for Data Scientists - Scikit-Learn Specialization
Scikit-Learn, also known as Sklearn, is a free, open-source machine learning (ML) library used for the Python language. In February 2010, this library was first made public. And in less than three years, it became one of the most popular machine learning libraries on Github.Scikit-learn is the best place to start for access to easy-to-use, top-notch implementations of popular algorithms. This library speeds up the development of ML models.The main features of the Scikit-learn library are regression, classification, and clustering algorithms (random forests, K-means, gradient boosting, DBSCAN, AND support vector machines). The Scikit-learn library also integrates well with other Python libraries, such as NumPy, Pandas, IPython, SciPy, Sympy, and Matplotlib, to fulfill different tasks.Python for Data Scientists: Scikit-Learn Specialization presents you with a hands-on, simple approach to learn Scikit-learn fast.
How Is This Book Different?
Most Python books assume you know how to code using Pandas, NumPy, and Matplotlib. But this book does not. The author spends a lot of time teaching you how actually write the simplest codes in Python to achieve machine learning models.In-depth coverage of the Scikit-learn library starts from the third chapter itself. Jumping straight to Scikit-learn makes it easy for you to follow along. The other advantage is Jupyter Notebook is used to write and explain the code right through this book.You can access the datasets used in this book easily by downloading them at runtime. You can also access them through the Datasets folder in the SharePoint and GitHub repositories.You also get to work on three hands-on mini-projects:

1. Spam Email Detection with Scikit-Learn
2. IMDB Movies Sentimental Analysis
3. Image Classification with Scikit-Learn

The scripts, graphs, and images in the book are clear and provide easy-to-understand visuals to the text description. If you're new to data science, you will find this book a great option for self-study. Overall, you can count on this learning by doing book to help you accomplish your data science career goals faster.
The topics covered include:

• Introduction to Scikit-Learn and Other Machine Learning Libraries
• Environment Setup and Python Crash Course
• Data Preprocessing with Scikit-Learn
• Feature Selection with Python Scikit-Learn Library
• Solving Regression Problems in Machine Learning Using Sklearn Library
• Solving Classification Problems in Machine Learning Using Sklearn Library
• Clustering Data with Scikit-Learn Library
• Dimensionality Reduction with PCA and LDA Using Sklearn
• Selecting Best Models with Scikit-Learn
• Natural Language Processing with Scikit-Learn
• Image Classification with Scikit-Learn

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