Learn to code and build apps! Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you. There are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.

In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Science and Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, and Neural Networks. You'll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data.

This course is a comprehensive, practical guide to using MongoDB and Spark in Python, learning how to store and make sense of huge data sets, and performing basic machine learning tasks to make predictions. MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. NoSQL databases require a new way of thinking about data and scalable queries. Once Mongo queries have been mastered, it is necessary to understand how we can leverage this API in Python's rich analysis and visualization ecosystem. This course will cover how to use MongoDB, particularly if you are used to SQL databases, with a focus on scalability to large datasets.

Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is the next big thing and a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R is one of the most popular programming languages among the data science professionals. So, if you're a data science professional who wants to learn machine learning and deep learning with R, then go for this Learning Path.

SurveyUSA reported nearly three-quarters of seniors and residents in rural areas believe self-driving cars should not be allowed where they live. The disparities were even larger in terms of gender, as almost twice the number of males would like to see autonomous vehicles in their neighborhood as females. Respondents with a four-year college degree showed the highest support for autonomous vehicles, at 30 percent, followed by those who graduated high school and those who have some college education, respectively.

Alison Gopnik's career began with a psychology experiment she now considers ridiculous. Aiming to understand how 15-month-olds connect words with abstract concepts (daddy caregiver), she decided to visit nine kids once a week for a year. The then Oxford graduate student would record everything they said as part of her dissertation. "It was absurd for a million reasons," says Gopnik, holed up on a winter Friday in her office at the University of California at Berkeley, where she is a professor of developmental psychology. "If a childhad moved away, if there weren't any take-aways after the year, or any number of things, all that work would have been gone," she says, before adding, "I would never allow a student of mine to do anything like that today."

Keras is a deep learning library written in Python for quick, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because of its lightweight and very easy to use nature, Keras has become popularity in a very short span of time. So, if you are a data scientist with experience in machine learning with some exposure to neural networks, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

This course was funded by a wildly successful Kickstarter. Do you want to predict the stock market using artificial intelligence? In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and make a stock market prediction app. We interweave theory with practical examples so that you learn by doing. AI is code that mimics certain tasks.

If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you. There are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment. We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don't do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do.

In today's world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. Machine learning gives you unimaginably powerful insights into data. Deep learning is the next step to machine learning with a more advanced implementation.