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Showing posts with the label ML

Don't be fooled by machine learning and deep learning

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Don't be fooled by machine learning and deep learning Deep learning and machine learning are the most popular words in the world of artificial intelligence. But do you know what deep learning and machine learning are? In fact, they are both parts of artificial intelligence. Although the parts of the same technology sound the same, they are completely different, but complementary to each other. Before understanding these two, let me explain what is artificial intelligence.

Artificial Intelligence and Machine Learning

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 The reference While presenting the Interim Budget for the year 2018-19, Union Minister Piyush Goyal also referred to Artificial Intelligence ie Artificial Intelligence. What is the government's plan for artificial intelligence? The government will open a National Center for Artificial Intelligence and soon the Artificial Intelligence Portal will also be launched. The new Artificial Intelligence Portal will be created in support of the National Program on Artificial Intelligence. The Artificial Intelligence program will be used to benefit people from Artificial Intelligence and its associated technology. Its national centers will be built which will act as hubs, currently, its 9 areas have been selected. The government has not given any information about what will happen in the proposed Artificial Intelligence Portal and how the people will be benefited from it. What is Artificial Intelligence? In 1955, John McCarthy first used the term Artificial Intelligence, which is why it is a

Unsupervised Machine Learning

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Unsupervised Machine Learning Unsupervised machine learning algorithms infer patterns from a dataset without reference to known or labeled, outcomes. Unlike supervised machine learning, unsupervised machine learning methods cannot be directly applied to regression or a classification problem because you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. Unsupervised learning can instead be used to discover the underlying structure of the data. Why? It purports to uncover previously unknown patterns in data, but most of the time these patterns are poor approximations of what supervised machine learning can achieve. Additionally, since you do not know what the outcomes should be, there is no way to determine how accurate they are, making supervised machine learning more applicable to real-world problems. The best time to use unsupervised machine learning is when you do not have data on desired outcome

Supervised Machine Learning

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Supervised Machine Learning It provides you with a powerful tool to classify and process data using machine language. With supervised learning you use labeled data, which is a data set that has been classified, to infer a learning algorithm. The data set is used as the basis for predicting the classification of other unlabeled data through the use of machine learning algorithms. ▪   Linear Regression, and ▪  Classification Techniques. Linear Regression It is a supervised learning technique typically used in predicting, forecasting, and finding relationships between quantitative data. It is one of the earliest learning techniques, which is still widely used.  For example, this technique can be applied to examine if there was a relationship between a company’s advertising budget and its sales. You could also use it to determine if there is a linear relationship between particular radiation therapy and tumor sizes. Classification Techniques The classification techniques that will be discu

Prerequisite for Machine Learning

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Prerequisite for Machine Learning To get started with Machine Learning you must be familiar with the following concepts: Statistics Linear Algebra Calculus Probability Programming Languages Statistics Statistics contain tools that can be used to get some outcome from the data. There are descriptive statistics which is used to transform raw data into some important information. Also, inferential statistics can be used to get important information from a sample of data instead of using a complete dataset. To learn more about Statistics you can go through the following blogs: All You Need To Know About Statistics And Probability A Complete Guide To Maths And Statistics For Data Science Linear Algebra Linear algebra deals with vectors, matrices, and linear transformations. It is very important in machine learning as it can be used to transform and perform operations on the dataset. Calculus Calculus is an important field in mathematics and it plays an integral role in many machine learning

Demand for AI and machine learning experts

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Demand for AI and machine learning experts to grow by 60% by next year Automation in companies is expected to increase by 60 percent in demand for Artificial Intelligence and Machine Learning specialists by 2018. According to Francis Padang, regional director of talent management solutions provider, AI and machine adoption may be growing, but there is a shortage of experienced talent in technologies such as deep learning and neural networks. This is the salary of AI professionals Experience annually 2-4 years Rs 15-20  lakhs 4-8 years 20-25 lakhs 8-15 years 50 lakhs -1 crores At the same time, HR professionals believe that their role as AI evolves into broader and more strategy product management roles. Since there is a huge shortage of talent in the AI ​​sector, That's why employers who use AI automation tools will speed up the recruitment process. Telecom jobs increased due to 4G, 30 lakh recruitment to be done by 2018 There is a piece of good news for people sitting in governmen

Artificial Intelligence (A.I.) and Machine Learning (M.L.)

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Artificial Intelligence (A.I.) and Machine Learning (M.L.) For the last few years, artificial intelligence has remained in equal discussion on various causes and issues. Artificial intelligence is a branch of computer science whose job is to make intelligent machines. Recently, the government think tank NITI Aayog and Google have agreed that both with the aim of promoting India's Artificial Intelliegence-AI and Machine Learning-ML ecosystem. They will work together on several initiatives, which will help to create an ecosystem of artificial intelligence in the country. NITI Aayog has been entrusted with the responsibility of developing technologies like AI and preparing national programs for research. On this responsibility, the NITI Aayog is developing a National Action Policy on Artificial Intelligence along with the National Data and Analytics Portal, so that it can be widely used. It may be noted that DeepMind, Google's artificial intelligence company, is working on several

The Real-World Benefits of Machine Learning in Healthcare

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The real-world benefits of machine learning in healthcare It is safe Medicine has many manual procedures. During the training, I hand wrote lab values, diagnoses, and other chart notes on paper. I always knew this was the place where technology could help improve my workflow and hopefully it also improves patient care. Since then, the progress of electronic medical records has been remarkable, but the information they provide is no better than replacing old paper charts. If technology is to improve care in the future, then the electronic information provided to doctors must be enhanced by the power of analytics and machine learning. Using these types of advanced analytics, we can provide better information to doctors on the point of patient care. With easy access to blood pressure and other important signals, I have seen my patients become regular and expectant. Imagine how much more useful it would be if I could read the last 500 blood pressure readings, laboratory test results, race,