The Internet and the vast amount of data are inspirations for CEOs of big corporations to start to use Machine learning. It is to provide a better experience to users. How TensorFlow Starts Let us take Amazon, online retail that uses Machine learning. The algorithm's purpose is to generate revenue. Based on user search data, the ML application provides information or insights. The other example is the advertising platform where Google is a leader in this line. Where it shows ads based on the user movements while surfing the web. These are just a few, but there are many in reality. Machine Learning Evolution Top ML Frameworks Torch The torch is the first framework developed in 2002 by Ronan Collobert. Initially, IBM and Facebook have shown much interest. The interface language is Lua. The primary focus is matrix calculations. It is suitable for developing neural networks. Theano It is developed in 2010 by the University of Montreal. It is highly reliable to process graphs (GPU). The

I want to share with you the best real-time examples on machine learning.  Because of new computing technologies, machine learning today is not like machine learning of the past.  While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Machine learning use cases The heavily hyped, self-driving Google car? The essence of machine learning.  Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.  Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.  Fraud detection? One of the more obvious, important uses in our world today. Best example : "pattern recognition" is best example for Machine Learning Related: Take Quiz on Machine Learning Where can you apply machine learning. 

#What is machine learning algorithm Just a basics on Machine Learning Alice has just begun taking a course on machine learning. She knows that at the end of the course, she will be expected to have “learned”all about this topic. A common way of gauging whether or not shehas learned is for her teacher, Bob, to give her a exam. She has done well at learning if she does well on the exam. But what makes a reasonable exam? If Bob spends the entire semester talking about machine learning, and then gives Alice an exam on History of Pottery, then Alice’s performance on this exam will not be representative of her learning. On the other hand, if the  (The general supervised approach to machine learning: a learning algorithm reads in training data and computes a learned function f . This function can then automatically label future text examples .) exam only asks questions that Bob has answered exactly during lectures, then this is also a bad test of Alice’s learning, especially if it

According to Coursera-Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Real Meaning of Machine Learning Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Photo credit: Srini Role of Machine Learning More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Dream Job in Machine Learning Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Related: Course on Machine Learning In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation

Why you need machine learning skills? Let us start with Big data. Big data relates to extremely large and complex data. So, the availability of huge data makes machine learning is popular to use in future prediction. 6 ideas how to use ML in IoT Machine Learning comprises algorithms that learn from data, make predictions based on their learning, and have the ability to improve their outcomes with experience. Due to the enormity of data involved with Machine Learning, various technologies and frameworks have been developed to address the same. Hadoop is an open-source framework targeted for commodity hardware to address big data scale. The distributed design of the Hadoop framework makes it an excellent fit to crunch data and draw insights from it by unleashing Machine Learning algorithms on it.  So, the true value of IoT comes from ubiquitous sensors’ relaying of data in real-time, getting that data over to Hadoop clusters in a central processing unit, absorbing the s

According to Coursera -Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 1.      introduction, The Motivation Applications of Machine Learning 2.      An Application of Supervised Learning - Autonomous Deriving 3.      The Concept of Underfitting and Overfitting 4.      Newtons Method 5.      Discriminative Algorithms 6.      Multinomial Event Model 7.      Optimal Margin Classifier 8.      Kernels 9.      Bias/variance Tradeoff 10. Uniform Convergence - The Case of Infinite H 11. Bayesian Statistics and Regularization 12. The Concept of Unsupervised Learning 13. Mixture of Gaussian 14. The Factor Analysis Model 15. Latent Semantic Indexing (LSI) 16. Applications of Reinforcement Learning 17. Generalization to the Conti

Machine learning is a branch of artificial intelligence. Using computing, you will design systems. These systems to behave with AI features, from your end, you need to train them. This process is called Machine Learning. Read my  part-1 if you miss it. The life cycle of machine learning Acquisition - Collect the data  Prepare - Data Cleaning and Quality  Process- Run Machine Tools  Report- Present the Results Acquire Data You can acquire data from many sources; it might be data that are held by your organization or open data from the Internet. There might be one data set, or there could be ten or more. Cleaning of Data You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the prepare phase. Running Machine Learning Scripts The processing phase is where the work gets done. The machine learning routines that you have created perform this phase. Reporting Finally, the

The following are the list of languages useful for Machine learning. There's no such thing as one language being "better" than another. It's a case of picking the right tool for the job. Your Resume has value if you put any one of these languages. Python The Python language has increased in usage because it's easy to learn and easy to read. Python has good libraries such as scikit-learn, PyML, Jython and pybrain. R R is an open-source statistical programming language. The syntax is not the easiest to learn, but I do encourage you to have a look at it. It also has a large number of machine learning packages and visualization tools.  The R-Java project allows Java programmers to access R functions from Java code. Matlab The Matlab language is used widely within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualizations and graphs. Scala A new breed of languages is emerging that takes advantag