Machine Learning with Python Course and E-Book Bundle

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models, and is one of the most important new frontiers in technology. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. Over this course you’ll explore some of the possibilities of deep learning, and how to use TensorFlow to process data more effectively than ever.

• Access 22 lectures & 2 hours of content 24/7
• Discover the efficiency & simplicity of TensorFlow
• Process & change how you look at data
• Sift for hidden layers of abstraction using raw data
• Train your machine to craft new features to make sense of deeper layers of data
• Explore logistic regression, convolutional neural networks, recurrent neural networks, high level interfaces, & more

Python is the general purpose, multi-paradigm programming language that many professionals consider one of the best beginner language due its relative simplicity and applicability to many coding arenas. This course assumes no prior experience and helps you dive into Python fundamentals to come to grips with this popular language and start your coding odyssey off right.

• Access 43 lectures & 4.5 hours of content 24/7
• Learn variables, numbers, strings, & more essential components of Python
• Make decisions on your programs w/ conditional statements
• See how functions play a major role in providing a high degree of code recycling
• Create modules in Python
• Perform image manipulations w/ Python

You’ve seen deep learning everywhere, but you may not have realized it. This discipline is one of the leading solutions for image recognition, speech recognition, object recognition, and language translation – basically the tools you see Google roll out every day. Over this course, you’ll use Python to expand your deep learning knowledge to cover backpropagation and its ability to train neural networks.

• Access 19 lectures & 2 hours of content 24/7
• Train neural networks in deep learning & to understand automatic differentiation
• Cover convolutional & recurrent neural networks
• Build up the theory that covers supervised learning
• Integrate search & image recognition, & object processing
• Examine the performance of the sentimental analysis model

Every business wants to gain insights from data to make more informed decisions. Data mining provides a way of finding these insights, and Python is one of the most popular languages with which to perform it. In this course, you will discover the key concepts of data mining and learn how to apply different techniques to gain insight to real-world data. By course’s end, you’ll have a valuable skill that companies are clamoring to hire for.

• Access 21 lectures & 2 hours of content 24/7
• Discover data mining techniques & the Python libraries used for data mining
• Tackle notorious data mining problems to get a concrete understanding of these techniques
• Understand the process of cleaning data & the steps involved in filtering out noise
• Build an intelligent application that makes predictions from data
• Learn about classification & regression techniques like logistic regression, k-NN classifier, & mroe
• Predict house prices & the number of TV show viewers

You see graphs all over the internet, the workplace, and your life – but do you ever stop to consider how all that data has been visualized? There are many tools and programs that data scientists use to visualize massive, disorganized sets of data. This e-book contains content from “Data Visualization: A Successful Design Process” by Andy Kirk, “Social Data Visualization with HTML5 and JavaScript” by Simon Timms,” and “Learning d3.js Data Visualization, Second Edition” by Andrew Rininsland and Swizec Teller, all professionally curated to give you an easy-to-follow track to master data visualization in your own work.

• Harness the power of D3 by building interactive & real-time data-driven web visualizations
• Find out how to use JavaScript to create compelling visualizations of social data
• Identify the purpose of your visualization & your project’s parameters to determine overriding design considerations across your project’s execution
• Apply critical thinking to visualization design & get intimate with your dataset to identify its potential visual characteristics
• Explore the various features of HTML5 to design creative visualizations
• Discover what data is available on Stack Overflow, Facebook, Twitter, & Google+
• Gain a solid understanding of the common D3 development idioms

Get a complete introduction to the many uses of Python in this curated e-book drawing content from “Python 3 Object-Oriented Programming, Second Edition” by Dusty Phillips, “Learning Python Design Patterns, Second Edition” by Chetan Giridhar, and “Mastering Python Design Patterns” by Sakis Kasampalis. Once you’ve got your feet wet, you’ll focus in on the most common and useful design patterns from a Python perspective. By course’s end, you’ll have a complex understanding of designing patterns with Python, allowing you to develop better coding practices and create systems architectures.

• Discover what design patterns are & how to apply them to writing Python
• Implement objects in Python by creating classes & defining methods
• Separate related objects into a taxonomy of classes & describe the properties & behaviors of those objects via the class interface
• Understand when to use object-oriented features & when not to use them
• Explore the design principles that form the basis of software design, such as loose coupling, the Hollywood principle, & the Open Close principle, & more
• Use Structural Design Patterns to find out how objects & classes interact to build larger applications
• Improve the productivity & code base of your application using Python design patterns
• Secure an interface using the Proxy pattern

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Consequently, professionals who can run machine learning systems are in high demand and are commanding high salaries. This e-book will help you get a grip on advanced Python techniques to design machine learning systems.

• Learn to write clean & elegant Python code that will optimize the strength of your algorithms
• Uncover hidden patterns & structures in data w/ clustering
• Improve accuracy & consistency of results using powerful feature engineering techniques
• Gain practical & theoretical understanding of cutting-edge deep learning algorithms
• Solve unique tasks by building models
• Come to grips w/ the machine learning design process

Data science is one of the most in-demand fields today, and this e-book will guide you to becoming an efficient data science practitioner in Python. Once you’ve nailed down Python fundamentals, you’ll learn how to perform data analysis with Python in an example-driven way. From there, you’ll learn how to scale your knowledge to processing machine learning algorithms.

• Implement objects in Python by creating classes & defining methods
• Get acquainted w/ NumPy to use it w/ arrays & array-oriented computing in data analysis
• Create effective visualizations for presenting your data using Matplotlib
• Process & analyze data using the time series capabilities of pandas
• Interact w/ different kind of database systems, such as file, disk format, Mongo, & Redis
• Apply data mining concepts to real-world problems
• Compute on big data, including real-time data from the Internet
• Explore how to use different machine learning models to ask different questions of your data

Python is one of the most popular programming languages today, enabling developers to write efficient, reusable code. Here, you’ll add Python to your repertoire, learning to set up your development environment, master use of its syntax, and much more. You’ll soon understand why engineers at startups like Dropbox rely on Python: it makes the process of creating and iterating upon apps a piece of cake.

• Master Python w/ 3 hours of content
• Build Python packages to efficiently create reusable code
• Creating tools & utility programs, and write code to automate software
• Distribute computation tasks across multiple processors
• Handle high I/O loads w/ asynchronous I/O for smoother performance
• Utilize Python’s metaprogramming & programmable syntax features
• Implement unit testing to write better code, faster