Self driving cars thought to be a distant dream just a few decades ago. However, thanks to the recent progress made in various fields of computer science, this dream is becoming a reality now. Computer vision plays a central role in understanding the capabilities these vehicles required to be able to operate not only under standard conditions, but also under the most unexpected situations. Machine Learning is everywhere these days. We live in a world where Machine Learning and Artificial Intelligence is not obscure mathematical and science fiction anymore they have become crucial part of our lives.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Artificial Intelligence, increasingly relevant in the modern world where everything is driven by technology and data, is the process of automating any system or process to carry out complex tasks and functions automatically, in order to achieve optimal productivity. This video explains the basics of AI using popular Java-based libraries and frameworks to build your smart applications. We will cover easy-to-complex artificial intelligence tasks such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation with the practical approach we mentioned earlier. By the end of this video, you will have a solid understanding of Artificial Intelligence concepts. You will be able to build your own smart applications for multiple domains, as required.

This complete guide will teach you how to build and deploy Machine Learning models on your mobile device with TensorFlow Lite. You will understand the core architecture of TensorFlow Lite and the inbuilt models that have been optimized for mobiles. You will learn to implement smart data-intensive behavior, fast, predictive algorithms, and efficient networking capabilities with TensorFlow Lite. You will master the TensorFlow Lite Converter, which converts models to the TensorFlow Lite file format. This course will teach you how to solve real-life problems related to Artificial Intelligence--such as image, text, and voice recognition--by developing models in TensorFlow to make your applications really smart.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. If you're interested to know the statistical data analysis techniques and implement them using the popular Java APIs and libraries, 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 Learning Path starts by showing you the various techniques of pre-processing your data.

Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you to create your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together.

Data science is an ever-evolving field, with exponentially growing popularity. Data science includes techniques and theories extracted from the fields of statistics, computer science, and most importantly machine learning, databases, and visualization. So, if you're a developer who wants to enter in the field of data science by exploring concepts of statistics, data analysis, and data mining, then follow this Learning Path. Packt's Video Learning Path is 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. This Learning Path begins with explaining the steps to analyse data and identify which summary statistics are relevant to the type of data you are summarizing.

Are you interested to explore advanced algorithm concepts such as random forest vector machine, K- nearest, and more through real-world examples? 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. Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! It explores the study and construction of algorithms that can learn from and make predictions on data.

"Honestly Mike your classes speak for themselves. They're informative, concise and just really well put together. They're exactly the kind of courses I look for." This is the second course in a series designed to prepare you for becoming a machine learning engineer. I'll keep this updated and list only the courses that are live.

This is the fourth course in the series designed to prepare you for a real world job in the machine learning space. I'd highly recommend you take the courses serially. People love building models and many think that machine learning engineers sit around and build models all day. Take the courses in order to understand what machine learning engineers really do. In this course we are going to learn SciKit-Learn using a lab integrated approach.