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Machine Learning with Python Course and E-Book Bundle

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22

Deep Learning with TensorFlow

Discover One of the Newest & Most Useful Tools for Deep Learning

By Packt Publishing | in Online Courses

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
Dan Van Boxel is a Data Scientist and Machine Learning Engineer with over 10 years of experience. He is most well-known for "Dan Does Data," a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Getting Started
    • The Course Overview (2:59)
    • Installing TensorFlow. (5:33)
    • Simple Computations (5:31)
    • Logistic Regression Model Building (6:58)
    • Logistic Regression Training (4:53)
  • Deep Neural Networks
    • Basic Neural Nets (5:16)
    • Single Hidden Layer Model (5:06)
    • Single Hidden Layer Explained (4:32)
    • Multiple Hidden Layer Model (5:22)
    • Multiple Hidden Layer Results (4:43)
  • Convolutional Neural Networks
    • Convolutional Layer Motivation (5:03)
    • Convolutional Layer Application (6:55)
    • Pooling Layer Motivation (3:58)
    • Pooling Layer Application (4:17)
    • Deep CNN (6:28)
    • Deeper CNN (4:08)
    • Wrapping Up Deep CNN (4:55)
  • Recurrent Neural Networks
    • Introducing Recurrent Neural Networks (9:02)
    • skflow (9:19)
    • RNNs in skflow (4:04)
  • Wrapping Up
    • Research Evaluation (6:55)
    • The Future of TensorFlow (4:18)

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Lifetime
Content
4.5 hours
Lessons
43

Beginning Python

Interested In Coding? Start with Python

By Packt Publishing | in Online Courses

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
William Fiset is a Mathematics and Computer Science Honors student at Mount Allison University with in interest in competitive programming. William has been a Python developer for +4 years, starting his early Python experience with game development. He owns a popular YouTube channel that teaches Python to beginners and the basics of game development.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Python Basics
    • The Course Overview and Installing Python (6:14)
    • Setting Up a Programming Environment (6:46)
    • Variables (5:29)
    • Introduction to Types (6:03)
    • Basic Operators (5:52)
  • String Manipulation
    • Introduction to Strings (6:25)
    • String Functions (6:42)
    • Advanced String Manipulation (5:22)
    • String Formatting (8:17)
    • User Input (5:18)
  • Lists
    • Introduction to Lists (6:24)
    • List Methods (4:51)
    • Advanced List Methods (5:40)
    • Built-in List Functions (4:25)
    • 2D Arrays and Array References (7:44)
    • List Slicing (5:46)
  • Conditionals
    • Control Flow (5:45)
    • Comparison Operators (4:44)
    • Else and Elif (6:43)
    • and, or, and not (6:09)
    • Conditional Examples (5:22)
    • Mini Program (7:45)
  • Loops and Iterables
    • For Loop (7:25)
    • While Loop (6:44)
    • Iterables (4:52)
    • Loops and Conditionals (5:00)
    • Prime Number Checker (6:55)
  • Functions
    • Function Basics (5:29)
    • Parameters and Arguments (6:57)
    • Return versus Void Functions (3:33)
    • Working with Examples (8:29)
    • Advanced Examples (6:46)
    • Recursion (4:26)
    • Recursion Examples (9:10)
  • Modules
    • Import, as, and from (4:42)
    • Python API and Modules (6:47)
    • Creating Modules (4:59)
    • Modules and Testing (5:28)
  • Python and Picture Manipulation
    • Installing PIL/Pillow (6:28)
    • Basics of Using PIL/Pillow (6:25)
    • Picture Manipulations (6:29)
    • Custom Picture Manipulation (6:20)
    • Wrapping Up (3:03)

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2 hours
Lessons
19

Deep Learning with Python

Answer Big Data Questions by Learning How to Master Neural Networks

By Packt Publishing | in Online Courses

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
Eder Santana is a PhD candidate in Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Head First into Deep Learning
    • The Course Overview (3:51)
    • What Is Deep Learning? (4:08)
    • Open Source Libraries for Deep Learning (4:30)
    • Deep Learning "Hello World!" Classifying the MNIST Data (7:57)
  • Backpropagation and Theano for the Rescue
    • Introduction to Backpropagation (5:23)
    • Understanding Deep Learning with Theano (5:04)
    • Optimizing a Simple Model in Pure Theano (7:54)
  • Keras – Making Theano Even Easier to Use
    • Keras Behind the Scenes (5:24)
    • Fully Connected or Dense Layers (4:46)
    • Convolutional and Pooling Layers (6:40)
  • Solving Cats Versus Dogs
    • Large Scale Datasets, ImageNet, and Very Deep Neural Networks (5:17)
    • Loading Pre-trained Models with Theano (5:15)
    • Reusing Pre-trained Models in New Applications (7:22)
  • "for" Loops and Recurrent Neural Networks in Theano
    • Theano "for" Loops – the "scan" Module (5:18)
    • Recurrent Layers (6:28)
    • Recurrent Versus Convolutional Layers (3:43)
    • Recurrent Networks –Training a Sentiment Analysis Model for Text (6:50)
  • Bonus Challenge and TensorFlow
    • Bonus Challenge – Automatic Image Captioning (4:40)
    • Captioning TensorFlow – Google's Machine Learning Library (5:15)

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Lifetime
Content
2 hours
Lessons
21

Data Mining with Python

Learn Data Mining by Actually Doing It

By Packt Publishing | in Online Courses

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
Saimadhu Polamuri is a data science educator and the founder of Data Aspirant, a Data Science portal for beginners. He has 3 years of experience in data mining and 5 years of experience in Python. He is also interested in big data technologies such as Hadoop, Pig, and Spark. He has a good command of the R programming language and Matlab. He has a rudimentary understanding of Cpp Computer vision library (opencv) and big data technologies.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introduction to Data Mining
    • The Course Overview (3:55)
    • A Brief Introduction to Data Mining (4:37)
    • Data Mining Basic Concepts and Applications (7:05)
  • Setting Up the Data Mining Python Packages Environment
    • Why Python? (3:31)
    • Basics of Python (5:55)
    • Installing IPython (2:09)
    • Installing the Numpy Library (4:32)
    • Installing the pandas Library (5:32)
    • Installing Matplotlib (2:42)
    • Installing scikit-learn (2:37)
  • Cleaning Data and Preprocessing Techniques
    • Data Cleaning (5:31)
    • Data Preprocessing Techniques (5:08)
  • Linear Regression Model
    • Linear Regression Basic Model Approach (8:23)
    • Evaluating Regression Models (5:31)
    • Basic Regression Model Implementation to Predict House Prices (9:20)
    • Regression Model Implementation to Predict Television Show Viewers (9:46)
  • Classification Concepts
    • Logistic Regression (4:01)
    • K – Nearest Neighbors Classifier (5:51)
    • Support Vector Machine (5:41)
    • Logistic Regression Model Implementation (10:45)
    • K – Nearest Neighbor Classifier Implementation (10:43)

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Data Visualization: Representing Information on the Modern Web E-Book

Study the Tools & Processes of Visualizing Data In This Comprehensive E-Book

By Packt Publishing | in Online Courses

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
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Data Visualization: Representing Information on Modern Web
    • Data Visualization: Representing Information on Modern Web

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Python: Master the Art of Design Patterns E-Book

Enhance Your Professional Design, Development, & Architecture Coding Abilities

By Packt Publishing | in Online Courses

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
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • First Section
    • Python: Master the Art of Design Patterns

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Python: Deeper Insights into Machine Learning E-Book

Develop Predictive Analytical Models with Python & Earn a Higher Salary

By Packt Publishing | in Online Courses

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
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • First Section
    • Python: Deeper Insights into Machine Learning

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Python: Real-World Data Science E-Book

Unleash the Power of Python & Its Robust Data Science Capabilities

By Packt Publishing | in Online Courses

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
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • First Section
    • Python: Real-World Data Science

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Lifetime
Content
3 hours
Lessons
37

Mastering Python

Write Maintainable, Reusable Code to Power Your Development Process

By Packt Publishing | in Online Courses

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
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done –whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Setting Up the Work Environment
    • The Course Overview
    • Downloading and Installing Python
    • Using the Command Line and the Interactive Shell
    • Installing Packages with pip
    • Finding Packages in the Python Package Index
  • Creating a Package
    • Creating an Empty Package
    • Adding Modules to the Package
    • Importing One of the Package's Modules from Another
    • Adding Data Files to the Package (2:33)
  • Basic Best Practices
    • PEP 8 and Writing Readable Code
    • Using Version Control
    • Using venv to Create a Stable and Isolated Work Area
    • Getting the Most Out of docstrings Part 1 – PEP 257 and Sphinx
    • Getting the Most Out of docstrings Part 2 – doctest
  • Creating a Command-line Utility
    • Making a Package Executable via python – m
    • Handling Command-line Arguments with argparse
    • Text-mode Interactivity
    • Executing Other Programs
    • Using Shell Scripts or Batch Files to Launch Programs
  • Parallel Processing
    • Using concurrent.futures
    • Using Multiprocessing
  • Coroutines and Asynchronous I/O
    • Understanding Why Asynchronous I/O Isn't Like Parallel Processing
    • Using the asyncio Event Loop and Coroutine Scheduler
    • Futures
    • Making Asynchronous Tasks Interoperate
    • Communicating across the Network
  • Metaprogramming
    • Using Function Decorators
    • Using Function Annotations
    • Using Class Decorators
    • Using Metaclasses
    • Using Context Managers
    • Using Descriptors
  • Unit Testing
    • Understanding the Principles of Unit Testing
    • Using unittest
    • Using unittest.mock
    • Using unittest's Test Discovery
    • Using Nose for Unified Test Discovery and Reporting

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