Update: This course has been updated to include 8 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future! If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them. So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand. This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer. The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects. The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples! 9 Projects That Are Included in This Course: Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project. Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud. Project 3 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking. Project 4– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network. Project 5 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality. Project 6 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language Processing by solving a text classification task using multiple classification algorithms. Project 7 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset. Project 8 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering. All of this and so much more is included in this course. So, what are you waiting for? Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Machine learning and Big data analytics are the most future looking skillset. Are you ready to upgrade your skills? Around 85% of companies were likely to adopt AI and ML algorithm to run their business, therefore it will increase job opportunities as well as stiff competition. Even Big data analytics also playing a vital role in finding meaningful insights from unstructured big data.  Meaningful insights will help business  to understand customer needs and changes in the trends. This course will cover ML and big data analytics services offered by Microsoft Azure. ML services includes LUIS, QnA Maker, Computer vision, Content moderator, Translator, Text Analytics whereas for big data analytics service includes   Stream Analytics, Data Lake and Data Analytics using HDInsight with Apache Spark, Jupyter and Zappeline. Microsoft Azure is one of the popular cloud computing platform where you'll  deploy all mentioned services. Topics covered in this learning path: Simple chatbot integrates in HTML websites Echo Bot Facebook Chat bot Question and Answer Maker LUIS (Language Understanding) Text Analytics Detecting Language Analyze image and video Recognition handwritten from text Generate Thumbnail Content Moderator Translate and many more things In this course, you'll learn machine learning, data analytics and also cloud computing as well. All of them are most trending domain of IT . So enroll this course and gain skills to beat the thriving competition .

This course will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. Students, at the end of this training, will be technically competent in the basics and the fundamental concepts of Machine Learning such as: Understand components of a machine learning algorithm Apply multiple machine learning tools to build and evaluate predictors on real data Learn how to perform different classification algorithm to filtering the Email data Forecasting on Time series Data Perform Clustering with the help of case study This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture.

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.