Machine learning is transforming the way businesses operate, and being able to understand trends and patterns in complex data is becoming critical for success. Python can help you deliver key insights into your data by running unique algorithms and statistical models. Covering a wide range of powerful Python libraries, this book will get you up to speed with machine learning.
- Access 454 pages of content 24/7
- Find out how different machine learning techniques can be used to answer different data analysis questions
- Learn how to build neural networks using Python libraries & tools such as Keras & Theano
- Write clean & elegant Python code to optimize the strength of machine learning algorithms
- Discover how to embed your machine learning model in a web application
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns & structures in data w/ clustering
Data science and machine learning are some of the top buzzwords in the tech world today. This course is your entry point to machine learning! More companies than ever are relying on data mining to make informed business decisions and data scientists are in increasing demand. This course will put you on track for a lucrative career in Big Data.
- Access 45 lectures & 5 hours of content 24/7
- Get an introduction to machine learning & the Python language
- Learn important concepts like exploratory data analysis, data preprocessing, feature extraction, classification, & more
- Acquire the mechanics of several important machine learning algorithms
- Gather a broad picture of the machine learning ecosystem & master best practices
- Tackle data-driven problems & implement your solutions w/ Python
Designed to introduce you to the most relevant and powerful machine learning techniques used by today’s top data scientists, this book delivers clear examples and detailed code samples to demonstrate deep learning techniques, semi-supervised learning, and more. The techniques covered in this book are at the forefront of commercial practice and will help you break into this lucrative, growing industry.
- Compete w/ top data scientists by gaining a practical & theoretical understanding of cutting-edge deep learning algorithms
- Apply your new found skills to solve real problems
- Automate large sets of complex data & overcome time-consuming practical challenges
- Improve the accuracy of models & your existing input data using feature engineering techniques
- Use multiple learning techniques together to improve the consistency of results
- Understand the hidden structure of datasets using a range of unsupervised techniques
- Improve the effectiveness of your deep learning models further by using ensembling techniques to strap multiple models together
Machine learning is pervasive in the modern, data-driven world. It’s used in search engines, robotic, self-driving cars, and many more instances. In this course, you’ll learn how to perform various machine learning tasks in many different environments. Focusing on real-life scenarios, you’ll learn how to solve real problems and use Python to implement algorithms.
- Access 97 lectures & 4.5 hours of content 24/7
- Deal w/ various types of data & explore the differences between machine learning paradigms
- Cover a range of regression techniques, classification algorithms, predictive modeling, & more
- Use real-world examples to solve real-life problems
Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large data sets is a growing need. This course uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.
- Apply the most scalable machine learning algorithms
- Work w/ modern state-of-the-art large-scale machine learning techniques
- Increase predictive accuracy w/ deep learning & scalable data-handling techniques
- Improve your work by combining the MapReduce framework w/ Spark
- Build powerful ensembles at scale
- Use data streams to train linear & non-linear predictive models from extremely large datasets using a single machine
Apache Spark has become one of the most popular tools in machine learning because it can handle huge data sets at incredible speed. This book shows you Spark at its very best, demonstrating how to connect it with R to unlock maximum value from the tool and your data. These blueprints will reveal some of the most interesting challenges that Spark can help you tackle.
- Set up Apache Spark for machine learning and discover its impressive processing power
- Combine Spark & R to unlock detailed business insights essential for decision making
- Build machine learning systems w/ Spark that can detect fraud & analyze financial risks
- Create predictive models focusing on customer scoring & service ranking
- Design recommendation systems using SPSS on Apache Spark
- Tackle parallel computing & find out how it can support your machine learning projects
- Turn open data & communication data into actionable insights by making use of various forms of machine learning
TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. This course addresses common commercial machine learning problems using Google’s TensorFlow library, familiarizing you with this powerful tool.
- Access 19 lectures & 1.5 hours of content 24/7
- Discover how to use TensorFlow & use it in real-world use cases
- Cover unique features like Data Flow Graphs, training, & visualization of performance w/ TensorBoard
Python is a general purpose and a relatively easy to learn programming language, making it the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This book helps bridge the gap between machine learning and web development. You’ll focus on the Python language, frameworks, tools, and libraries, and eventually build a machine learning system.
- Get familiar w/ the fundamental concepts & some machine learning jargon
- Use tools & techniques to mine data from websites
- Grasp the core concepts of Django framework
- Get to know the most useful clustering & classification techniques and implement them in Python
- Acquire all the necessary knowledge to build a web application w/ Django
- Build & deploy a movie recommendation system application using the Django framework in Python
OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is a great choice to solve real-world computer vision problems and how you can use the OpenCV modules to implement popular machine learning concepts.
- Access 12 lectures & 1.5 hours of content 24/7
- Learn how to work w/ various OpenCV modules for statistical modeling & machine learning
- Discuss supervised & unsupervised learning, and how to implement them w/ real-world examples
- Implement efficient models using classification, regression, decision trees, K-nearest neighbors, & more
Machine learning is the process of teaching machines to remember data patterns, use them to predict future outcomes, and offer choices that would appeal to individuals based on past preferences. Learning to build machine learning alogirthms within a controlled test framework will speed up your time to deliver, quantify quality expectations, and enabled rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms.
- Get started w/ an introduction to test-driven development & familiarize yourself with how to apply these concepts to machine learning
- Build & test a neural network deterministically, and learn to look for niche cases that cause odd model behavior
- Learn to use the multi-armed bandit algorithm to make optimal choices
- Generate complex & simple random data to create a wide variety of test cases
- Develop models iteratively, even when using a third-party library
- Quantify model quality to enable collaboration & rapid iteration
- Adopt simpler approaches to common machine learning algorithms
- Take behavior-driven development principles to articulate test intent
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