Being primarily a course that deals with statistics and training the machine to think independently, machine learning is not restricted to just working on developing codes or updating the algorithms.
On average, a freshly graduated machine learning engineer with no experience can earn around Rs 7 lakhs to Rs 8 lakhs per annum. Engineers with around 4 years of experience can expect to be paid around Rs 9 lakhs per annum. Machine learning engineers with over 5 years of experience can earn around Rs 14 lakhs per annum. A senior machine learning engineer with over 10 years of experience can earn around Rs 20 lakhs to Rs 25 lakhs per annum.
SKILLS | PAY SCALE P/A |
Python | Rs 6 lakh |
Deep Learning | Rs 7.5 lakh |
Natural Language Processing(NLP) | Rs 7 lakh |
Computer Vision | Rs 7.3 lakh |
There are many specializations available in the field of Machine Learning, offering attractive salary packages based on the knowledge, experience and performance of professionals.
As mentioned above, machine learning and data science are similar courses and are inter-related but these two have different applications. If you have a degree in machine learning with data science as an elective, you can easily become a data scientist. The salary for data scientists can start from Rs 6 lakh per year.
As a computational intelligence expert with machine learning, you are responsible for programming a computer that can understand human languages. The computational linguist is also tasked with conducting research on how the brain works in processing a language and applying the same principles to an artificial neural system. This field is related to linguistics and biology. Hence, you can easily expect to make an earning close to Rs 20 lakh per annum.
Deep learning is an advanced process that involves machine learning that would program artificial neural networks to help a computer perform thought processing more like human beings. It is still in a developing stage; hence the tasks require you to have a deep understanding of the subject. The primary work includes defining data and subsets of data. With these definitions and labeling of data, deep learning computers can be trained to identify other sets. You can expect to make about Rs 15 lakh to Rs 18 lakh per year.
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Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn.
Learners will apply Q-Learning to train an RL agent to play the game of numerical Tic Tac Toe.
Create a solution that will help in identifying the type of complaint ticket raised by the customers of a multinational bank
Build a machine learning model capable of detecting fraudulent transactions. Here you have to predict fraudulent credit card transactions with the help of machine learning models.
Build a neural network from scratch in Tensorflow to identify the type of skin cancer from image.
Make a Smart TV system which can control the TV with user’s hand gestures as the remote control
Build a model to using the concepts of natural language processing and recommender systems to recommend news stories to users on a popular news platform.
Learners will use the Markov Decision Process & Q-Learning to build an RL agent that learns to choose the best request so as to maximize the total profit earned by the agent that day.
You will build a custom NER to get the list of diseases and their treatment from a medical healthcare dataset.
Build a model that can help any visually impaired person in understanding image present before them.
Build a sentiment analysis based product recommendation system to recommend the similar products to the users. Sentiment analysis is used to fine tune the product recommendation system.
Predict the sales for a european pharma giant using a host of different types of variables. Apply VAR and VARMAX models to build the appropriate model
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa. CycleGAN model is used for producing T2 type MRI images given T1 type input MRI images
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa.
Create a custom object detector using the YOLO algorithm to detect the presence of face masks in the images of different people.