Overview of this book
<p><span id="description" class="sugar_field">Machine learning and data analysis are the center of attraction for many engineers and scientists. The reason is quite obvious: its vast application in numerous fields and booming career options. And Python is one of the leading open source platforms for data science and numerical computing. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. If you are among those seeking to enhance their capabilities in machine learning, then this course is the right choice.</span></p>
<p><span id="description" class="sugar_field">Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and focused recipes for data analysis and scientific computing. This course tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. You will apply state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. In short, you will be well versed with the standard methods in data science and mathematical modeling.</span></p>
<p><span id="description" class="sugar_field">The code bundle for the video course is available at: <a style="font-weight: normal;" href="https://github.com/PacktPublishing/Statistical-Methods---Applied-Mathematics-in-Data-Science." target="_new">https://github.com/PacktPublishing/Statistical-Methods---Applied-Mathematics-in-Data-Science.</a></span></p>
<h1><span class="sugar_field">Style and Approach</span></h1>
<p><span class="sugar_field"><span id="trade_selling_points_c" class="sugar_field">This practical, hands-on course will teach you how to analyze and visualize all kinds of data in Jupyter Notebook.</span></span></p>
