The field of Data Science and Machine Learning has witnessed a boom over the years. All the majority of the data produced has been very vast and particular to handle. And all this has surprisingly happened in the last 10 years of human mankind.
And so with the new advanced technologies we’re moving towards a more data-driven industry and society.
Data Science and Machine Learning
These are one of the most emerging fields in the subparts of the data world.
Ever since the computer is invented we’re trying to make them smarter and more powerful. From abacus, to room-sized machines to desktops , and to pocket-sized computers.
Google Assistant or Siri or Cortana are some of the most common applications of Machine Learning in our phones in technological field.
These tools may help you in the following ways:
1 Finding nearest places for job
2 Assisting in the Arrangement of the schedule
3 Setting up reminders for deadlines
4 Making calls on your behalf
5 To continue a conversation
6 Play music according to preferences
7 Searching pictures in your gallery
The more you use it the more useful it becomes. The learning graph and it’s process is continous.
That is how it is with all the machine learning products and services out there.
Another real-world example in the field of medical diagnosis was explained by Tom Gruber in his ted talk. He said that we have the power whether we choose ML and AI to Automate and compete with us or Augment and collaborate with us.
How ML helps in detection of cancer
On a normal day, people spend their entire day in labs looking at 100 of samples of cancer exposed cells.
The diagnosis of predicting whether the person has cancer as by AI enabled machine is as follows
The diagnosis of predicting whether the person has cancer as by humans is as follows
When AI and human services are joined together the accuracy reaches to the highest potential as compared to them alone.
Imagine how much of cancer is now being able to get detected which would have otherwise left untreated.
That’s how Machine Learning and AI helps with the partnership of human abilities which creates humanistic approach to problems.
Another example in the field of engineering,
Who is responsible for providing mechanical designs to an automobile system parts.
An engineer has knowledge about the most feasible designs suiting the various factors of automotive system.
But there is a limit to how creative human abilities can move to.
Similarly if you are given an ML model, it is like a chance to discover different designs.
It further provides thousands of different designs within a few minutes.
That’s how efficient technology can be.
What is Machine Learning?
Machine learning is the application or sub branch of artificial intelligence (AI) that provides computers the ability to learn themselves on their own and automatically improve from their experience without being programmed in advanced by the programmers which helps in making decisions faster, better and more accurately.
The main focus of Machine Learning (ML) is on the development of computer programs. These programs will be able to access data and learn about the future processes.
It is also the study of computer algorithms that improve automatically through experience and over time.
The idea behind it is that systems can learn from data, identify patterns and make decisions with minimum human intervention.
For example, in the field of image processing, medical predictions, classification, learning association, regression, clustering etc. The smart systems built on machine learning algorithms have the capability to learn from past experience or historical data that is available.
From driving cars and translating speech, from making recommendation systems to fraudulent detections, machine learning is building an explosion in the capabilities of artificial intelligence helping softwares make sense of the messy and unpredictable unstructured/ structured data.
What is the need for machine learning?
The need arises for more advanced and complex tasks. Because it is challenging for humans to manually create algorithms to solve a specific problem.
While development a common observation is made. The work is more effective when the machine develops its own algorithm rather than having human programmers specify every step in process.
Machine learning algorithms find natural hidden patterns and anomalies in data. These algorithms generate insights and help us make better decisions and predictions over time.
They are used everyday to make critical decisions in medical diagnosis, stock trading, fraudulent detections , forecasts and more.
Impact of Machine Learning to real world
1 .Let’s take the example of the most basic amenities of life- “Food”.
With the help of computer vision and machine learning we can drive a great impact on the growth of the agricultural resources.
With more faster processing and less human intervention, lesser inputs pertaining higher outputs, won’t only help the farmers of different countries but will also help in a lot more trades connected with it.
2. What if just by sitting home, just with your finger tip , not consulting to a doctor and paying lots of money, you can know what’s wrong with your body?
A smart system based on machine learning has been adopted by a start up which provides machines that can tell you ,your body’s disease just with a drop of your blood.
3. How can you know if putting in your money to watch a film would be worthwhile or not? Either you can ask to your close friends or relatives or if given a chance would you look at the reviews of thousands of people out there summed up to a ratio, and then make a decision.
Sentimental analysis in machine learning and AI brings you the best reviews.
4. The virtual assistants, language translators, maps, almost every product that is available virtually, ranging from your shopping apps, to music ones or even food delivery applications, each and every product is using machine learning at it’s base for better productivity, efficiency, lower risks, to understand their clients better and growth in their respective businesses.
5. From Personal Computers to 10 years down the lane Web technology to again another 10 years later to Phone technology in the late 2000’s we’re now shifting from-
Mobile first world to AI first World.
Computing will be universally available, people would be able to interact with it naturally and seamlessly.
The best example of it is Google which is working on it since more than a decade.
For example in Google assistant- It is universally available, translates your speech input into text, or understands what you need.
Photos, phones ,videos, trips, reservations, messages, calendars, and so on ,it provides a help with all of that, and bring a particular google for your own world with the help of machine learning.
Google not only uses AI, but tries to fit it in all of it’s products, ranging from voice recognition which makes google truly translational to natural language processing to image recognition to translation.
To approach human level accuracy even a percent of accuracy matters.
In other google products such as google image search,
Earlier machines could only see the objects, but now they can detect colors of images through computer vision.
Earlier, machines could not count,but now they can count and precisely tell the details about a picture and can suggest better furthermore.
Another product being google lens, which can tell you about the insights of the pictures just by scanning the pictures, and can also recommend stuff related to it.
Also using speech recognition, they can provide live captions to even the videos you record in your phone gallery, or any video that you surface online.
When you’re texting, google can work on smart reply and predict what can you say on next, with keeping your data with full privacy.
With computer vision in progress, now when you switch to the walking directions in your google map, you can actually see arrows on screen, instead of a blue dot.
What will be the impact of machine learning in next 5-10 years?
Growth of Artificial intellegence
How machine learning and Ai will get augmented in future with human intervention-
- Traditional programming is not going away.
- Machine Learning will augment most traditional tasks.
- We’ll get better at learning from small or noisy data.
- We’ll see ML systems be combined with traditional approaches.
Are you planning to start a career in Machine Learning?
Enroll yourself in the numerous courses, blogs, videos, articles and books available in the market related to Machine Learning and start learning for free.
In order to understand the very basics you must know the basics of any of the following programming languages-
Python, R, Java ,C++,
After you finish learning the above mentioned topics, start understanding the basics of DBMS (Database Management System) like sql.
Then understand the maths- stats, probability, calculus,
Later understand the machine learning algorithms.
For that you can take up any online course, ranging from Coursera to udemy or to the list that we’ve mentioned in one of our blogs with title-
Data has always been there with us, although making the use out of it in various approaches and using it efficiently has been a relatively new procedure. This field is ever growing and is changing at this exact moment as well, the use of stats, maths , computer science, algorithms is breathtaking only if you can find the courage to dive in something new everyday.