It is estimated that by 2027 the size of the global Machine Learning market will reach near about 117.19 Billion Dollars. Machine learning has used the power of recommendation systems which are now put to use by big companies like Netflix and Amazon. Not only this it is also expected to transform many industries and henceforth create new job opportunities in the coming years.
Since ML is at its rise it is a very obvious question that will dawn on your mind and that is how much time does it take to become an ML specialist. To answer this question correctly the actual amount of time that a person takes to learn machine learning can vary depending on the individual's background experience learning pace and how much time he is being able to dedicate to understanding the subject. Although studies have found that on average it may take around 6 to 12 months of dedicated learning and practice to become proficient in the field of machine learning. This time may be shorter or longer depending on the learning pattern of the learner.
As far as the salary is concerned an ML engineer in India earns around 8 lakh Indian rupees on average. However, the salary may range from 4 lakh to 15 lakh per year depending on factors like experience skill level location and company size. The demand for ml engineers is only increasing and more and more companies are adopting AI and machine learning technology giving rise to higher salaries for experienced professionals in the field.
Now that the specs of the subject are in clear view let's dive deeper into the branches of the tree called machine learning. Machine learning can be categorized into three algorithms namely supervised learning, unsupervised learning and reinforcement learning.
The dataset used for supervised learning is labeled, and the algorithm is performed on this labeled input and output data dataset. The algorithm learns to identify patterns between the data input and output and using this knowledge it predicts the output for new input data. Read more in detail about supervised learning from our detailed article on it:
Published on: May 14, 2023
#supervisedlearning, #labeleddata, #classification, #regression, #applications, #neuralnetworks, #randomforest, #fittingmodel
For unsupervised learning the data set used is unlabeled and the algorithm is trained on this unlabeled data set. The algorithm is not given any information about the output data but instead, it is expected to identify patterns and relationships within the input data. Read more in detail about unsupervised learning from our detailed article on it:
Published on: May 14, 2023
#unsupervisedlearning, #unlabeleddata, #insights, #patterns, #applications, #predictions
Reinforcement learning is it type of machine learning that involves teaching an algorithm to make decisions based on the feedback. The algorithm learns by itself by using the trial and error method, receiving feedback in the form of rewards or penalties for specific actions. The goal to use this algorithm is to learn to make decisions that maximize the rewards that it receives. Read more in detail about reinforcement learning from our detailed article on it:
Published on: May 14, 2023
#reinforcementlearning, #agent, #rewards, #punishments, #feedback, #qlearning, #deepreinforcementlearning, #application
Further machine learning algorithms can be classified into regression classification clustering and neural networks. Regression algorithms are usually used to predict numerical values based on input data. Classification algorithms are primarily used to characterize input data into one or more classes. Clustering algorithms however I used to group input data into clusters based on similarities between the data points. And finally, neural networks are the type of machine learning algorithms that are designed to simulate how the human brain works often used for image recognition, natural language processing and other complex tasks.
Machine learning finds its applications in speech recognition, image processing, predictive analysis, fraud detection and many more. Overtime as the volume of data continues to grow and the computing power becomes more and more affordable and accessible the potential uses of machine learning are only increasing understanding of these different machine learning algorithms and how they can be used to influence businesses and individuals to help them be up to date and stay competitive in these present times where a rapid technological change can be seen. For a professional overview of ML and exploring what you might have missed, check out our following article:
Published on: March 20, 2023
#machinelearning, #datascience, #python, #education, #skills
#machinelearning, #supervisedlearning, #unsupervisedlearning, #reinforcementlearning, #classification, #regression, #applications
Published: May 14, 2023
Author: Dipti Vatsa
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