The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data. By- Uditha Bandara is specializes in Microsoft Development technologies.  He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).  He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India.

This is our epic course with 5 projects in artificial intelligence and machine learning: 01. Make your first Android app from scratch 02. Make your first machine learning model with the TensorFlow framework 03. Make an Android app that can analyze and predict handwritten digit data 04. Make an advanced app with the MNIST database of digits 05. Make an app that can predict the weather This course was funded by a wildly successful Kickstarter. Learn to build neuron functions, convolutional layers, and graphs to read handwritten digit data. Learn to code in Java and Python. Design the user interface of every app you make. Learn to maximize efficiency to build apps as quickly as possible and save time with good coding practices. Learn to test databases and build functions that connect to graphs. Train and test models that can detect credit card fraud. Test yourself with 8 coding exercises. Understand machine learning concepts that are shaping the future of app development. Create a credit card fraud detection model! Learn predictive modeling, logistic regression, linear regression, and regression analysis. Use PyCharm, Java & Android Studio to make apps using artificial intelligence. Learn with hands-on projects & examples! Do you want to learn how to use Artificial Intelligence (AI) for automation? In this course, we cover coding in Python, working with TensorFlow, and analyzing credit card fraud. We interweave theory with practical examples so that you learn by doing . Intro to TensorFlow for Android We from Mammoth Interactive are here to tell you that your Android and iOS apps can become smarter, stronger and more convenient thanks to machine learning. Better yet, we’ll show you how to build your very own intelligent software that grows with you. A wildly successful Kickstarter funded this course Machine learning is changing the world around us. It’s bringing us self-driving cars, facial recognition and artificial intelligence. ML began on computers, but the next big wave is machine learning for mobile. Have you ever thought: why can’t my mobile device do more ? Specific languages and software you'll learn Python 3 : Flexible and comprehensive, Python is an easy-to-use language that we will use to write powerful machine learning programs and other scripts. Java : Object-oriented and reliable, Java is one of the most widely-used languages and has been the choice language of Android applications for years. PyCharm 2017: Write, compile and run Python code in one convenient location. Android Studio 3: Build Android apps by providing both graphical and programming interfaces for front and back end functionality. TensorFlow: Allows us to build computational graphs & neural networks and perform intense tasks like training and optimizing models with ease. Become a mobile machine learning guru! We'll see you inside! We blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection. Join Mammoth Interactive in this course now.

What if you could build a character that could learn while it played?  Think about the types of gameplay you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course, we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics.  In addition she's written two award winning books on games AI and two others best sellers on Unity game development. Throughout the course you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques, distilling the mathematics in a way that the topic becomes accessible to the most noob of novices. Learn how to program and work with: genetic algorithms neural networks human player captured training sets reinforcement learning Unity's ML-Agent plugin Tensorflow Contents and Overview The course starts with a thorough examination of genetic algorithms that will ease you into one of the simplest machine learning techniques that is capable of extraordinary learning. You'll develop an agent that learns to camouflage, a Flappy Bird inspired application in which the birds learn to make it through a maze and environment-sensing bots that learn to stay on a platform. Following this, you'll dive right into creating your very own neural network in C# from scratch.  With this basic neural network, you will find out how to train behaviour, capture and use human player data to train an agent and teach a bot to drive.  In the same section you'll have the Q-learning algorithm explained, before integrating it into your own applications. By this stage, you'll feel confident with the terminology and techniques used throughout the deep learning community and be ready to tackle Unity's experimental ML-Agents. Together with Tensorflow, you'll be throwing agents in the deep-end and reinforcing their knowledge to stay alive in a variety of game environment scenarios. By the end of the course, you'll have a well-equipped toolset of basic and solid machine learning algorithms and applications, that will see you able to decipher the latest research publications and integrate the latest developments into your work, while keeping abreast of Unity's ML-Agents as they evolve from experimental to production release. What students are saying about this course: Absolutely the best beginner to Advanced course for Neural Networks/ Machine Learning if you are a game developer that uses C# and Unity. BAR NONE x Infinity. A perfect course with great math examples and demonstration of the TensorFlow power inside Unity. After this course, you will get the strong basic background in the Machine Learning. The instructor is very engaging and knowledgeable. I started learning from the first lesson and it never stopped. If you are interested in Machine Learning , take this course.

This course has been prepared for professionals aspiring to learn the basics of R and Python and develop applications involving machine learning techniques such as recommendation, classification, regression and clustering. Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language like R and Python and its packages. After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video , and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark , which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

This course aims at making you comfortable with the most important optimization technique - Linear Programming. It starts with the concept of linear, takes you through linear program formulation, brings you at ease with graphical method for optimization and sensitivity, dives into simplex method to get to the nuances of optimization, prepares you to take advantage of duality and also discusses various special situations that can help you in becoming smart user of this technique.

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

If you are a developer, an architect, an engineer, a techie, an IT enthusiast, a student or just a curious person, if you are interested in taking on machine learning but you are not too sure where to start, this is probably the right course for you!! In this course, we start with the basics and we explain the concept of supervised learning in depth, we also go over the various types of problems that can be solved using supervised learning techniques. Then we get more hands-on and illustrate some concepts relative to data preparation and model evaluation with bits of code that you can easily reuse. And last, we actually train and evaluate several models based on the most common machine learning algorithms for supervised learning such as K-nearest neighbors, logistic regression, decision trees and random forests. I hope that you find this course fun and easy to follow and that it gives you the machine learning background you need to kick start your journey and be successful in this field!

Given the constantly increasing amounts of data they're faced with, programmers and data scientists have to come up with better solutions to make machines smarter and reduce manual work along with finding solutions to the obstacles faced in between. Python comes to the rescue to craft better solutions and process them effectively. This comprehensive 2-in-1 course teaches you how to perform different machine learning tasks along with fixing common machine learning problems you face in your day-to-day tasks. You will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will also use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. Further to get a complete hold on the technology, you will work with tools using which you can build predictive models in Python. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. In the first course, Getting Started with Machine Learning in Python , you will learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. You will then use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will also learn to understand and estimate the value of your dataset. Next, you will learn how to clean data for your application, and how to recognize which machine learning task you are dealing with. The second course, Building Predictive Models with Machine Learning and Python , will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems. By the end of this course, you will be able to take the Python machine learning toolkit and apply it to your own projects to build and deploy machine learning models in just a few lines of code. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action. Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed-back into how our AI generates content. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a Machine Learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and Machine Learning. Quantitative trading was also a great platform from which to learn about reinforcement learning in depth, and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

The easiest way to learn and do various machine learning in the world.Lectures that will definitely satisfy the beginners. Lectures that will surprise any skilled person.Lectures that make you become familiar with the machine through machine learning.Lectures that make you wait for the next lectures.You will learn how to conduct, compare, validate and present a variety of machine learning and their result.Sample data for all lectures are given.Free unlimited tools to try it out are given.