What is machine learning ?

Machine learning was first used by multinational organizations such as Facebook, Google, and Amazon. Google used it for advertisement placement while Facebook used Machine learning for showing the post feeds. 

Machine learning is part of the umbrella of technology widely known as artificial intelligence (AI) focused on creating systems that learn from historical data, identify patterns in learning, and make logical decisions that require little to no human interaction. In short, it is a method of data analysis involving a variety of digital information, i.e., numbers, words, clicks, and images.

Machine learning applications are able to learn from data input and continue to improve upon the accuracy of the output with the use of automated optimization methods. The overall quality of a machine learning model depends upon the following:

1. Machine learning requires high-quality input data.

Much like a garden requires quality fertilizer to grow, a machine learning model requires high-quality data to get the best outcome. Low-quality, or inaccurate data, will yield a poor output.

2.Machine learning requires a high-quality machine learning model.

There are a ton of algorithms a data scientist can choose to meet their needs. It is important to choose the algorithm best suited for each use case. More complex neural networks are popular for some algorithms because they tend to be more accurate and versatile. However, a simpler model will often perform better when using a lower amount of data.

Starting with a proven machine learning model is imperative because it is more likely to accurately find features and patterns in data. The better the data, the better decisions and predictions the machine will be able to make.

Why is machine learning important to modern business ?

1. Availability of a wide range of data in large volumes.

2. Wide, affordable access to computational power.

3. Vast access to high-speed internet.

These factors make it easy for companies to develop computational models that can quickly and accurately analyze super-complex data sets.

Machine learning is being used to cut costs, minimize business risk, and improve quality of life. This can include making product recommendations, exposing potential cyber security threats, powering self-driving cars, and even labeling an X-ray as cancerous or not. As time moves forward, we are sure to see more examples of how machine learning can improve life across the spectrum.

But what can machine learning do, realistically, to help move technology forward ?

We can start by busting a few of today’s machine-learning myths.

You hear people talking about machine learning. But are you sure what is the truth and what’s a myth ? People are curious to know about machine learning and artificial intelligence but face a lot of confusion while getting started. Let’s get started with a few of them.

Myth #1 : Machine learning is more intelligent than humans.

As alredy mentioned above, machine learning was first used by multinational organizations such as Facebook, Google, and Amazon. Google used it for advertisement placement while Facebook used Machine learning for showing the post feeds. However, there are a lot of myths about machine learning and its impact. Let’s get started with a few of them

There is no doubt about machine learning’s powerful ability to find patterns and correlations using available data sets. However, at this point, humans are still needed to intervene to make assessments on the quality of results.

Using the example of a medical diagnosis, machine learning is able to quickly review available data. However, doctors and supporting medical professionals are needed to rule out inconsistencies in findings.

Myth #2 : Machine Learning can be used anywhere

This is one common myth that machine learning can be used anywhere. Nobody will spend Rs. 1,000 on work worth Rs.200 rupees. Machine learning is used only if you have Big Data sets. It is not worth using machine learning for small data solutions as that can be done by a human effortlessly.

Myth #3 : Machine learning will take over jobs.

While modern industry is seeing more robots automate manual work in places like factories, production facilities, and medical surgeries, the implementation – at this moment – is more of an assistive technology and not a replacement for human minds and hands. In fact, machine learning has made modern business practices more efficient via the simplification of repetitive processes.

Myth #4 : Machine Learning never changes.

Cyber security is a great example of how machine learning is always expanding out of necessity. The machine learning algorithms of today’s cyber security environment will no longer work in the next few weeks to months ahead. Why? Because criminals are always finding new ways to overtake technology for their own purposes. While machine learning models may be routine in a factory or warehouse, cyber security machine learning models will always have to be built from scratch.

Myth #5 : Machine Learning requires more data to get reliable results.

If you are a data scientist, it may make sense to add more data points to a machine learning model. This may not always be the best use of data. If an enormous amount of data is dumped into a machine learning model, there is risk in creating a model that memorized the information, leading to a case of model overfitting. This can also result in high error rates for unseen data. Your machine model needs that garden-fertilizer data for high-quality output. It also requires high-quality data to have a better chance at building the best machine learning model.

Myth #6 : Machine Learning can predict the future.

It is partially true companies can use machine learning to predict the future. But machine learning models can only predict the future if future events have some relevance or connection to past events. For example, there are some machine learning models that use past stock prices to predict future stock prices. Also, the weather can be predicted based on past weather information. Yet if a machine model is asked to make aprediction based on information that was not input prior to the development of the model, the prediction will not be dependable.

Myth #7 : There’s No Difference Between Artificial Intelligence And Machine Learning

Most often we use machine learning and artificial intelligence terms inter changeably. However, both are not the same and not synonymous with each other. Robotics, computer vision, and natural language processes are areas under the artificial intelligence stream. Machine learning is learning about patterns, using statistics and data predictions.

Myth #8 : Machine Learning Can Work Independently Without Human Intervention

People have a belief that the machine learns the system without real programming codes. However, the algorithms for machine learning solutions are developed by humans. So human intervention is a mandatory part of machine learning it can’t be ruled out entirely.

Myth #9 : Machine Learning Platform Is Easy To Build, And Anyone Can Do It

Many people think that you can just Google about machine learning and easily build any platform. However, machine learning is a special technique that demands the expertise skill set. To learn machine learning, you should know how to prepare the data for testing and training, how to demarcate data, how to build an exact algorithm and very important, you should know about the productive system. To get expertise in machine learning, one should have hands-on experience with machine learning patterns and algorithms.

Myth #10 : Machine Learning can work independently without human intervention

People have a belief that the machine learns the system without real programming codes. However, the algorithms for machine learning solutions are developed by humans. So human intervention is a mandatory part of machine learning it can’t be ruled out entirely.

 Myth #11 : Machine Learning Is The Future

In the future, no doubt machine learning can be used widely, but this is not the only future. There are more advanced technologies in the market that can be one step above machine learning. Self-driven car or robots have just been an imagination a few years back. However today it is a reality.

Theuse of machine learning is expected to grow. As the internet and data sets become more powerful, we can expect that companies will choose to utilize machine learning to solve some of the most basic business problems.

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