Why is ML Imperative for Leveraging Data?

While digitalization has swept through industries aggressively, the urgency to realize the full potential of data has increased significantly over the years. Organizations big and small, private and public, all want to reap the benefits of data science. Leaders today want to make decisions based on precise and accurate insights driven by data analytics. Data has been defined as the ‘new oil’ for industries and if effectively used it can transform even small start-ups into established industry leaders!

However, we generate an enormous amount of data every day and at an astonishing speed. Collecting, ingesting, and analyzing this enormous volume of data at a sustainable speed is not only difficult but also incredibly complex. Moreover, since no data is ignorable organizations looking to leverage data must look for innovative technologies capable of handling large data chunks on a real-time basis. Machine Learning is one such technological innovation with the capacity of handling Big Data and coupled with the principles of data science it is a very efficient tool for organizations intending to reap the benefits of data analytics. 

The importance of Machine Learning can be understood from the trend of increasing demand for engineers trained in Machine Learning across industries and individuals skilled in ML are scoring big in terms of lucrative employment and fat pay packages. The demand is especially high in economies like that India where there is a dearth of professionals skilled in Machine Learning but the industry is rapidly progressing towards a digital ecosystem driven by data. In such an atmosphere, individuals looking to upskill with ML must go for the best machine learning courses online or offline. 

Understanding Machine Learning

Machine Learning (ML) is an advanced domain of Artificial Intelligence that has been instrumental in making some ground-breaking applications in the field of digital technology. ML enables machines to learn through experience (data) and perform tasks without being explicitly programmed for each task. It can be supervised or unsupervised and at an Age when automation has become vital for industries, ML experts have become indispensable for both the private and public sector institutions. 

How Machine Learning helps businesses?

Rapid digital transformation has led to the onset of the fourth industrial revolution. Automation is the need of the hour and businesses must have a digitally driven strategy to stay relevant. The strategical use of sophisticated technologies like ML is thus has become very important. From introducing smart systems capable of performing automatically to improving the overall efficiency of an organization, ML is the key. Let us take a look at a few examples:

  • Netflix with a net valuation of USD150 billion attributes much of its success to its fascinating recommendation engine. Ever wondered how the video-streaming platform new exactly what you wanted to watch next or how does it able to recommend movies that you liked but never heard of before? Well, a very good ML engine is behind that magic. The engine has fed itself with data provided by your viewing choices and habits and is capable enough to read your mind! The personalized recommendation introduced by Netflix is behind the reason why the platform has left behind almost all its competitors!
  • Virtual personal assistants are now part of our everyday life and ML is responsible for that. Products like Siri, Alexa, and Google Now have redefined our relationship with machines and how we perceive them. Thus, for a smarter future, we need to take full advantage of ML.
  • Financial frauds have been disastrous for economies, especially in countries like India. Now that we have an enormous volume of financial data and ML, we can create systems with the ability to detect financial anomalies in real-time. Such systems are already in place in various parts of the world and are hugely successful.
  • The timely diagnosis of diseases like cancer and several other rare diseases is vital for a healthier future for our species. However, frequently human experts have their hands full and especially in countries like India where patients to doctor ratio are very low, such diseases are always fatal. Now, we can take advantage of ML-based algorithms to feed machines with historical data and create systems capable of diagnosis. Such systems are already in place and have proved more accurate than their human counterparts!

Lucrative employment opportunities in ML

ML skills are very important if you are aspiring to land lucrative jobs in the field of technology which includes:

  • Machine learning engineer
  • Data scientist
  • Data analytics
  • Big data expert
  • Computational Linguist

Such job roles offer great pay packages and growth opportunities for anyone with the requisite skills. For instance, in India an ML engineer earns anything between 70,000 USD to 146,000 USD, depending on the level of experience and skills. With firms like Accenture, PWC, and Fractal analytics hiring ML experts regularly, enrolling into the best machine learning courses online in India is immensely beneficial for anyone daring to dream big!

Should you be acquiring ML skills in India?

India is now a major player in the market of innovative technologies like AI and ML. The government envisions that based on such disruptive technologies; India will be able to grow its GDP at an astonishing rate. Companies are also investing in Indian businesses to develop infrastructure needed for rapid adoption of ML and AI. 

However, you must learn acquire the skills required to become an expert in Machine Learning and make your foundations very strong!

Three skills that you must have for learning ML:

Advantages of learning Python for ML:

  • Python is an easier alternative when compared to R or any other programming language. Even if you do not have any coding background, you will be able to learn Python and use it for ML.
  • Python is highly preferred by professionals as Python offers a great library ecosystem. Python libraries such as NumPy and Pandas let you perform many tasks like accessing, handling, and manipulating data. This in turn is very helpful to users as ML requires continuous processing of data. 
  • Another reason why Python is popular is that it is the most flexible programming language.  Users can easily combine Python with any other language and there is also no need of recompiling the source code! 

Probability and statistics
A clear understanding of probability and statistics will help you immensely to succeed in ML. Machine Learning algorithms are based on the characterization of probability and techniques derived from it and if you have a good knowledge base in the field of probability and statistics you will master ML algorithms in no time. 

Probability and statistics are also important because much of the work in the field of ML has to deal with uncertainties of the real world and predict events accurately. A prime example is the recommendation engine used by Netflix. It is capable of understanding the taste of a user and recommending movies based on that and is largely successful because of its advanced ML algorithm. 

Data Modelling and Evaluation

Data Modelling is the science of predicting properties of unseen instances through error detection and classification etc. This helps an ML expert or a data scientist to estimate the structure of a data set to extract useful information from it. Another part of Data modeling and evaluation is to maintain the accuracy of a certain model using certain validation techniques. Some important concepts in this context are:

  • Confusion Matrix
  • Logarithmic Loss
  • Classification accuracy

Thus, make sure to start your journey today through an online or offline certification course on Machine Learning!

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