Introduction to machine learning with Python: a guide for data scientists
Have you ever thought about how Google translation works? Or, have you ever imagined how your phone camera detects your face ? or how Elon Musk made a self-driving car? Probably yes, but what’s the science behind that? The camera doesn’t have eyes to see. Do cars do ‘t have neither upper nor lower limbs to drive. Isn’t it? Then, what makes these all things to happen on their own? Well, to understand this, let me take you to the 1950s, where a crazy scientist named Arthur Samuelused the phrase ‘ Machine Learning ‘ in 1952. But at that time, it was just a start. A start that never thought to come this way at such a great pace and made the humans so reliant on it. As humans, we have become so dependent on technology that we can’t progress in any form. We can’t stop using the data, which probably contains the secret of success hidden in it. Think of the day when programmers need not do anything on any machines. They need to sit with a buddy with coffee in hand and give instructions to devices and guess what machines will learn on their own from the data they collected in the past.
What is Machine learning?
Do you remember when we were so curious to learn everything, whatever we see as a child? Likewise, Machine Learning has the same role. Machine learning is a subset of Artificial Intelligence. A programmer writes a set of codes implicitly and uses the number of libraries and modules to make the system learn independently without being programmed explicitly. Even the topic clarifies itself about the meaning. Machine learning makes the system work as a human. The computer will understand things from the data it receives. It will examine the data set to make conclusions and retrieve fruitful data from it. The most crucial role of Machine Learning is to interpret the data which it had never seen before. The primary platform where programmers write Machine Learning codes in Python. Python has become one of the loved platforms because of its versatility, easiness, and library number. Python has superseded many of the reliant languages on which humans are dependent for a long time.
Data scientists have reduced the gap between humans and technology with the aid of machine learning and high-level algorithms. They have played a key role in creating and implementing the models and algorithms to provide the desired future pace. The need for Machine learning is not just to make humans life cherishable but to flourish them with an abundance of machine power and the machine’s brain. It won’t be bad enough to consider that computers are more efficient and accurate in decision-making and data analysis. It is not only bounded just towards business and corporates but towards health mechanisms. The vital role ML had played in the past in terms of predicting health, cost, and utilization is adorable and essential for human beings. It had played a significant role in the National security of different countries, fitted with different kinds of recognition cameras in drones, Hi-Tech and advanced monitoring systems, facial recognition techniques, and many more, from self-automated cars to various virtual assistants.
The technologies we have developed from ML are half of what to come shortly. It had advanced human life so profoundly that we have never expected up. And there is a lot more to come. A lot, which we can’t even think of. Now it depends on you to either work in 9 to 5 jobs or work to change all of humankind’s future.