One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

By the end of this course you will have 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project. For each we will build a basic version in PyCharm, save the trained model, export the trained model to Android Studio, and build an app around model. We'll give you all necessary information to succeed from newbie to pro. We will install PyCharm 2017.2.3 and explore the interface.

Data Structures and Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually. Algorithmic trading is not an attempt to make a trading profit. It is simply a way to minimize the cost, market impact and risk in execution of an order, but if you can't use this incredible tool, you might miss the right entry or exit spots that other traders will gladly take. What if you could change that?

This course is a go-to guide for the four topics, logic programming, heuristic search, genetic algorithms and building games with AI. It will help you learn to programme with AI. The course will start with the basic puzzles, parsing trees and expression matching. This will be followed by building solutions for region coloring and maze solving. The course also has fun-filled videos on building bots to play Tic-tac-toe, Connect Four and Hexapawn.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.

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.