Magnetic Talks on Intensifying Machine Learning Technology

What is Machine Learning in your point of view?

Humans learn from their experiences through the ability of rational thought. Machines cannot think rationally, rather it is taught by programming languages as input. Machine Learning is a part of Artificial Intelligence that gives capabilities to learn from pre-defined data and to progress itself if it realizes necessity. It works on the programmer's command and is used for storing data so that it improves by doing predictions.

How far-reaching are the applications of machine learning?

Machine learning is a buzzword in technology at present. You will be surprised to hear that you are surrounded by ML innovative applications. Let me tell you about the top 7 best ML applications.

1. Don't you search Google Maps when you are fastened in the most irritating traffic jam! ML gives you that immediate solution for faster route selection. But how does google map know that?
   Well, it is the merging of the number of people who are currently using the service, its database of historical traffic data, and a few technological tricks absorbed by other companies. Everyone who is using Google Maps is helping the app become a little more accurate and a little more indispensable for the rest of us.
2. Have you encountered automatic friend tagging suggestions ever while crawling through your Facebook or haven't you got suggestions whenever a photo is uploaded whether you want to tag your friends in that picture or not? Is it not common to us?
   Yes, ML is doing that through Facebook's face detection and recognization algorithm which is based on advanced deep learning research called deep face. It is a deep-learning facial recognition system created by a research group at Facebook. This system identifies human faces and digital images.
3. Not familiar with Uber, Ola, Rapido, Chalo! You may plan today to ride to travel with zero tension even in tempestuous times. It's good to know how these apps are functioning. When you are booking your ride, you are using the power of machine learning.
   This brought the greatest personalization into these apps. Firstly, it is simply asking for your destination including several predictions based on your habits and current location. If you are at the office, it can surmise that you want to return home or you want to go to a club, gym, etc. It is using an ML algorithm layered on top of historic trip data to make the estimated time of arrival information.
4. Have you seen ads recommendations on your browser? Yes, ML is used for generating this. Recommended ads are based on the user's search history.
5. Are you a Netflix worm? If yes, then you have surely visited the prescribed system of it. So, how do you think that each time you come to Netflix, you get a list of movies that are very similar to your interest?
   Good, this is also an ML application. Not only Netflix, and several market giants like YouTube, and Myntra, but Amazon and Spotify also use to target audiences based on their solitary interest by generating a recommended list of products, movies, or songs.
6. Heard about the awesome contrivance – Moley! It is the world's first robotic kitchen that can reproduce any dish cooked by a top chef from any part of the world. Moley has robotic hands like a human with corresponding speed sensitivity and gestures.
   In 2018, Moley launched the consumer version of this kitchen with the four-key integrated kitchen items of a robotic arm, oven, hob, and touchscreen unit. The entire kitchen system is remote-based. Even when it is not in use or no one is at home, it retracts from the view in robotic use. Its works are apprehended by many industries as well as restaurants.
7. Pretty sure that you must see chatbots on several websites. Have you chatted there? Do you know who is there from that side chatting with you? It is just an NLP (Natural Language Processing) feature of ML to collect details of users who visited their website.

Is Machine Learning about to die?

Right now, the biggest question on the mob's face is whether it is revolutionizing technology growth or not. What if there is a lack of data in the future? But why ? It is noticed that there is day-by-day rising popularity in the industry. Look at a glance at how ML is evolving!

In 1950, when a pioneering computer scientist Alan published an article answering the question can machines think. He brought forward the hypothesis stating that machines succeeded in persuading humans that it is not indeed a machine that would have achieved artificial intelligence. This was the Turing Test.

In 1957, Frank Rosenblatt sketched the first neural network for computers which is now known as the Perceptron model. The perceptron algorithm was designed to classify the visual inputs categorizing subjects into one of the two groups. The nearest neighbour algorithm was written in 1967.

Machine learning is being worked on and previously it is already thrashed out how ML research and applications got vast improvements based on a trial-and-error method for problem-solving. It is safe to say that there can never be a dearth of data.

What are the types of Machine Learning models?

ML models are used for making research more cumulative. For example, when you go shopping and have a plan for buying dresses for all of your family members, don't you think about the dress size for each person?

Suppose, your sister is dumpy, so you must look for an XXL size for her. If your aunt has a slim figure, you surely search for an L or XL-size dress. Here, figure type is taken as input, and dress size is the output. By using this labeled data in supervised learning, you can predict anything.

In unsupervised learning, there is no supervision. The machine has to identify data with the ability of pattern recognition, clustering, anomaly detection, association mining, and dimensionality reduction. In semi-supervised learning, algorithms are fed by a small amount of labeled training data. Reinforcement learning uses an agent and an environment to produce actions and rewards.

Is Machine Learning mitigating human performance?

Is that so? Machines will command humans one day? What will happen when there is not a single wrong prediction? Can machines bestow 100% accurate results?

Let's set forth that Machine Learning is just the combination of Data and algorithm. The machine will be revamped as humans instruct it with good-quality input. ML is just integrated with the human neural system so that it has the competence to learn from stored data as its experience without being programmed specifically.

Do you have fuzzy conceptions about Machine Learning?

We are here to fix your ambiguity. If you are an enthusiastic aspiring programmer and want to build in a machine learning zone, then you are welcomed hospitably. Know about Python, R, Numpy, Pandas, and Matplotlib. Explore Jupyter Notebook, Google Colab, Tableau, Power bi, and Kaggle for data representation.

There are several highly promising positions like Data Scientist, Data Analyst, Automation Engineer, Applied Researcher, Principal Software Engineer, Machine Learning Scientist, and many more that can be picked out if you have good skill sets. There is a high demand for Machine Learning engineers. In India, an entry-level Machine Learning salary is around Rs. 5 LPA per annum. A senior-level data scientist earns more than Rs. 17 LPA a year in India.

Conclusion

Machine learning has an insightful future by which technology can be developed for real-world data implementation and good-quality trained algorithms. If you are enthralled in predictive analytics, go with ML.