The term machine learning leaves us with the plenty of different meanings and definitions. So, picking up one definition of Machine Learning out of the heap is a bit tricky job. But, if we compile all the relevant definitions than we can say that the machine learning means learning from the experiences and examples instead of programs.
Companies like Facebook, Google and Amazon have got a lot of data about us. Even the small companies have got a lot of data like signup information, number of logins, Product purchase, products that we are looking for. All this data can be processed and can give any company a boost in productivity and increase in sale.
That is why machine learning is growing so fast.
Companies can offer amazing features like quick replies that are context based in Gmail, Uber driver arrival time or time to reach at the destination via Google maps, self-driving cars etc. This is just a start of machine learning and power of data science.
Welcome to data science and machine learning course!
One of the best online resource to understand and implement Machine learning and data science concepts. Usually, people think that data science can only be learned by Ph.D. but that not true, anyone can learn data science and machine learning.
What will you learn in this course?
We will start with python installing and getting a refresher on it. We will take some challenges like printing patterns, multiplication table, and web scrapping to refresh python memories.
After that, we will move to Anaconda distribution so that we don’t have to worry about installation anymore.
First we will deal with Numpy library. This is one library that every data science and Machine learning student should master.
The real fun begins with pandas. Bring data from HTML, css, xls or any database, pandas can handle almost everything.
One of the key aspect of data science is to understand the data and it’s pattern. This all is done using graphs. Matplotlib helps you to plot variety of graphs and deduce results from it.
Seaborn is another plotting library. But it is comparatively it is much easier and powerful. We will be analyzing 911 call dataset, iris flower dataset and Fat consumption through this library.
After we will learn about supervised and unsupervised and reinforcement learning. These are the core pillars and division criteria for all machine learning algorithms.
Machine Learning Algorithms
After that we will take on most common machine learning algorithms. We will take mathematics and code part of every algorithm like,
Linear Regression Algorithm Decision Tree Regression Algorithm K-nearest neighbors Algorithm Support Vector Machine classifier aka SVM Naive Bayes Algorithm
The main goal of this course is to make sure that you understand Machine learning and data science. Catch you inside the course.
From ages, we have heard stories of toothbrushes that can tell stories and microwaves that can automatically make food. Well, I haven’t lost my mind yet, because all these human imaginations have somehow taken the shape. As 20 years ago, we can’t imagine having a phone which can perform almost every task or have a wide world web named the internet. All this happened because of artificial intelligence.
From being dismissed as science fiction to becoming an intergral part of multiple,wildly popular movie series especially one starring Arnold Schwarzenegger, AI has been part of our lives longer than we realize.In fact, turing test , which was developed by Alan turing during the period of WW2,has been widely attributed as intelligence test for machines.As a field AI, as seen most Ups and downs in the past 50 years.On one hand it is hailed as a frontier of next technological revolution on other hand it is viewed with fear since it has potential to surpass human intelligence and hence achieve world domination.
Application for AI
Specialized applications of AI, however allow us to use facial recognition and image classification as well as smart personal assistants like Siri and Alexa. This usually leverage multiple algorithms to provide functionality to end-user, but may broadly be classified as AI.
Machine learning is a subset of properties commonly aggregated under AI techniques.The term was orginally used to describe the process of leveraging algorithms to parse data,build models that could learn from it , and ultimately make predictions using these learnt parameteres.
While it began as a small part of AI ,burgeoning interest has propelled ML to forefront of research and it is now used across domains. Growing hardware support as well as improvements in algorithms ,especially pattern recognition, has led ML being accessible for much larger audience, leading to wider adoption.
Applicaiton of ML
Initially, the primary application of ML were limited to field of computer vision and pattern recognition.This was prior to the stellar success and accuracy it enjoys today.Today we used ML without even being aware of how dependent are we, on day to day life. With Google’s search team trying to replace PageRank Algorithm with an improved ML algorithm named RankBrain, to facebook automatically suggesting friends to tag in a picture,we are surround by ML.
A key ML approach that remained dormant for a few decades was artificial neural networks. This eventually gained wide acceptance when improved processing capabilities became available. A neural network simulates the activities of brain’s neurons in a layered fashion , and the propagation of data occurs in similar a manner,enabling machines to learn more about a given set of observations and make accurate predictions.The accuracy of these models allows reliable services to be offered to end user, since the fall positives has been eliminated entirely .
Application of DL
DL has a large scale business applications because of its capacity to learn from million of observations at once. Although computationally intensive,it is still the preferred alternative because of its unparalled accuracy. Autonomous vehicles and recommendation systems ( Such as those used by Netflix and Amazon) are among the most popular applications of DL algorithms.
Embracing automation is a necessity in the current environment. It plays a key role in enabling productivity,improve customer service and enable businesses to grow more agile. This will trigger a new wave of growth in generating jobs. The key to individual is to be flexible and re-skill to develop and succeed.
The new concepts are swapping in the technology world every day and all these concepts are tightly interrelated with each other, that it becomes very hard for beginners to establish the difference between them. As right now, two terms “Artificial Intelligence” & “Machine Learning” are being used as each other synonym. That’s because to the first time user, both terms might seems same as basically they both are related to the automation of machine. But, in reality, both terms are different as day and night, they come in one category, but their basic structure is totally different. The main basic difference between both AI and machine learning is the utilization of Algorithm.
How exactly Algorithm separates AI & Machine Learning?
An algorithm is a set of complexed and simple calculation rules used to solve any problem. In machine learning, algorithms take in data and perform calculations to find an answer. It is the responsibility of algorithm to solve the problem in one significant manner. The performance of algorithm throughout depends on the ability of how independent algorithm is trained. So, we can say machine learning involves the algorithm for calculation and the ability of algorithm is related to its own intelligence. Well, it seems like machine learning and AI meets at one point, but before meeting they diverge their ways.
Fine Comparison between Artificial Intelligence & Machine Learning!
If we take in the realm of big data, both Artificial Intelligence and machine learning falls under one head but has an utterly different working model. We can say AI is bigger and wider concept than the machine learning. When we talk about intelligence ability of algorithm, then we are referring to artificial intelligence. But, when we think about data entry for calculation by the algorithm, then that would be machine learning. So, in nutshell, AI means the ability of algorithm and machine learning is data entry on which algorithm draws conciliation. Well, both works together yet very differently, that’s why people got confused in both terms.
Introduction to Deep Learning;
Apart from artificial intelligence and machine learning, a new term deep learning is buzzing nowadays also. Deep learning is another subset for machine learning, but its meaning is deeper and in the plural sense. The concept of deep learning is sometimes just referred to as “deep neural networks,” referring to the many layers involved. A neural network has one data involved, but in deep learning two or more data sections involved. And these sections can be seen as a nested hierarchy of related concepts or decision trees. A deep learning required more quality data as compared to machine learning.
Well, both artificial intelligence and machine learning are two separate concepts, but both need each other to perform dynamically. And in both having quality data is supreme to draw perfect calculations. So, in other sense working on AI and machine learning depends on quality data. For AI and machine learning to continue to advance, the data driving the algorithms and decisions need to be high-quality. In short, make your data high and your AI will be automatically high.
In WWDC 2017, Apple has made major announcements and one of the leading announcement is about the launch of ARKit. The ARKit is a very fine tool that has some amazing sensors and camera resolutions. The ARKit is the tool which removes the line between the reality and virtual reality to provide the implacable digital view. This tool will be introduced by the Apple in the iOS platform which is going to launch in the later this year. The ARKit is a developers tools which provide the ultimate platform to the developers to create argument reality apps on iOS.
The ARKit is a developing software which helps in creating argument reality apps on the iPhone and iPads. The AR apps combine real world with digital overlays, akin to Pokemon Go and Google Translate, which can translate text in the camera view in real time. In the Pokémon go game GPS system is used, but where as in the ARKit tech called SLAM (short for Simultaneous Localisation And Mapping) is used. SLAM allows ARKit to work on your iPhone or iPad camera to draw the outline of the object, and the sensor detects the important information in the image frame.
What ARKit offers to the app developers?
Apple has only given the little introduction to the ARKit in the WWDC 2017, they only provided the basic features of ARKit seen. But the ARKit is going to be a very cunning program to work with. Let’s me explain the ARKit functions with an example. The ARKit will take the experience of Apple flyover maps to the next extent. Select any city which supports Apple flyover, such as New York or San Francisco and by tapping on it will provide you the first person experience of traveling on the roads of cities. With the every tilt in the screen you will actually feel like walking on the busy streets of the city. This all credit goes to the ARKit of the Apple iOS.
Expectations from the ARKit?
The Apple and their fans have tremendously high expectations from the ARKit tool and they expect this tool to break down any walls between the virtual and reality. The Apple CEO, Tim Cook himself said that they are being lowed on the argument reality, but they have invested the huge chunk in it. So, this ARKit is going to make your iPhone and iPad really smart. According to the speculations going in the market, ARKit will enable users to view the animated characters to the real life. You can with the ARKit can select the pair of jeans from the e-commerce website and you can look how it will look on you before buying. The Pokémon go experience is going to amplify which is good news for the gamers. But to fully experience these features of ARKit on iOS 11, we have to wait for little.
Future of ARKit
May be Apple is little late with the argument reality as Google has already running project Tango. But Tango is only available on selected Android devices, and on the other hand, ARKit will be available on all the iOS devices. So, any way ARKit has bigger and brighter future with the exceptional features it is going to provide the iOS users.
Machine learning is something which every operating system wants to provide to their users. Like, always Apple took one step further and dreamt of launching user-friendly machine learning system. In WWDC 2017, the Apple main focus was on the machine learning and making it easier for the web developers to reach them. The mainly three machine learning systems of Apple are trending nowadays, Metal, a new computer vision framework, and Core ML: a toolkit that makes it really easy to put ML models into your app. Now in the article, we will take look brief at the machine learning experience with the iOS and how different it is from others.
This is the most talked feature of Apple’s machine learning and the simple features of it are very well preferred by the web developers. The API of Core ML is very simple and revolves around the main three functions. One can load trained models, making predictions and profit. Well, this seems to very fewer features, but one web developers here agree with me that how daunting task is to make one trained loaded model. Here Core ML offering this in very simple way.
The model is contained in a .mlmodel file. This is a new open file format that describes the layers in your model, the input and outputs, the class labels, and any preprocessing that needs to happen on the data. The everything is contained in the one open file which any one required to establish one file.
The next destination of the Apple machine learning train is vision framework. A vision framework is introduced in the iOS 11. Well, the vision allows users to perform computer vision related tasks. Before for the vision framework OpenCV is used by the phones, but now iOS has their own API to perform such operations. The functioning with the new vision framework is very simple and has numerous benefits.
The vision framework identifies the faces in the image and put them in the rectangular box. The advance vision framework can’t miss the single details of the facial features, like noise and eyes details can be easily seen through the vision framework. The every rectangular object can see and identify the road signs in the image with this power vision. Every bar code and technical saying can’t be missed. The joining to images is very productive task with this Apple vision framework.
The Metal Performance Shaders (MPS) is very popular and new iOS 11 has introduced some new features in it. As iOS 10 had few basic kernels for creating convolutional networks. Often it was necessary to write custom kernels to fill in the gaps. But in new iOS better and improved kennels are available. Now on the iOS, we have API for creating graphs. One can now create RNN, LSTM, GRU, and MGU layers. These work on sequences of MPSImage objects but also on sequences of MPSMatrix objects.
The Apple new API has numerous benefits but has some limitations also. Like, they are not open source, they have limitations and they’re only updated with new OS releases. But, still Apple APIs are very dynamic and the Apple team is working hard to improve its performance.