Machine Learning Research Opportunities for Undergraduates

Internship is an important phase in career that gives chance to work in real time and learn things practically and help to correlate theoretical knowledge with Practical. First, we will talk about Machine Learning and key features to get clear idea about what would be the target, career perspective.

About Machine Learning (ML):

“Machine learning is a data analysis process”. Now-a-days enterprises are being curious about the applications and benefits of machine learning in business. Many people don’t know about what ML is? & about how it is used to solve business related problems?

The Microsoft Azure, Google, Amazon are launching their Cloud Machine learning platforms. So artificial intelligence and ML have become prominent in the recent years. We have witnessed ML though we haven’t acknowledged it yet. For example, it witnessed in ‘Spam detection’ by email provider. It is also used in ‘Face' or 'Image' tagging by Facebook.

ML grasps ML algorithms to interactively learn from the existing data. It helps computers to search undisclosed insights without being programmed for. Your experiences about projects or internships in Machine Learning should reflect in your ‘Resume’. An aspirant must possess the ‘Intermediate level knowledge on own skills’ because companies will definitely not spend their money on you if you do not have any basic knowledge & skills.

Equip yourself with the required skills. Check the skill set companies have asked for. If you do not have the required skills, then you can join a professional training course to be a skilled candidate. One can join a course to get necessary skills. Internship in IT companies is referred to a wide spectrum of companies which cater to various domains and which use different technologies.

Companies shortlist the Interns after checking their Resumes & these companies don’t teach anything from scratch. Your resume gets shortlisted if it reflects your basic understanding about Machine Learning (ML) where you want to do internship in.

Internship in ML gives you this basic required knowledge. For this reason, Technogeeks provides Data Science course ‘hands on’ to give candidates ‘Practical Experience’ which helps them while joining IT companies. So, the complete Data Science course in Technogeeks is ‘Practically Oriented’.

After completion of internship Technogeeks gives :

1. Course completion certificate
2. Placement assistance by providing calls till the candidates get jobs in IT companies

A Technogeeks’ candidate learn following things during internship. Technogeeks covers following topics of Machine Learning:

To give candidates hands on experience, Technogeeks provides them:

1. Two live projects
2. Five POCs (Proof of Concepts)

There are Two types of Machine Learning projects:

1. Applied

Some important things helpful for obtaining an applied ML internship:

• Understanding about working of machine learning algorithms is necessary. Algorithms can fail and one must be aware of it when it happens. These algorithms make assumption about data.
• Knowledge about implementation and designing of ML pipelines with use of numerical computing libraries like NumPy, TensorFlow, PyTorch. Knowledge about implementation of these pipelines on high scale on cloud computing resources like AWS helps a lot.
• To work on big projects knowledge about Database Management is essential.
• Data collection and preprocessing are very important central elements for creation of Machine Learning systems.

2. Research

Some important things helpful for obtaining an “Research ML internship”:

• A candidate must be capable to carry out research through industry or through prior publications & PhD students generally perform better than undergraduate candidates as they have 3-4 years more research experience than undergraduates.
• Understanding about mathematical underpinnings of Machine Learning. Knowledge about classical algorithms like SVMs & logistic regression is essential. It includes knowledge in statistics, vector calculus, linear algebra & probability.
• Learn to read the research papers. It is a challenging task. Even if one has understanding about the recent developments in Machine Learning research community, that is not enough if the person is unable to critically analyze it because in the end a person must carry out implementation of these papers.

Advantages of studying Machine Learning:

• The unsupervised learning is a particular type of ML algorithm. ML models analyze the purchase history of a customer and then on that basis they identify products from customer’s product inventory in which a customer is interested. The algorithm finds undisclosed patterns among the items and then groups same products into clusters. This process is known as ‘c’, which is a specific type of ML algorithm. It is necessary to make better production recommendations for customers.
• ML consumes and identifies relevant data quickly. So offers reach to customers in a defined period.
• ML helps to create effective predictive maintenance plans for manufacturing firms. It decreases the possibilities of unanticipated failures which decreases redundant preventive maintenance activities.
• ML helps in spam detection. Because of ML, spam filters have made new rules using brain-like neural networks for eradication of spam mails. These neural networks identifies junk mail & phishing messages by appraising the rules across a vast computer networks.
• Data duplication and inaccuracy are the major issues confronted by organizations wanting to automate their data entry process & this grievance is addressed by Machine learning.
• It helps to make precise sales forecasts. ML assists enterprises for promotion of their products in a better manner and ML offers huge advantages to sales and marketing sector.
• In finance sector Machine Learning is useful for fraud detection, portfolio management, loan underwriting & algorithmic trading. It helps to improve the accuracy of financial rules & models as it detects & analyze nuances & anomalies.
• ML virtually consumes tremendous amount of comprehensive data. The consumed data is then used to continuously review sales and marketing strategies. For this it keeps checks on customer behavioral patterns. ML also helps to modify sales & marketing strategies.
• Because of ML, one can make analysis of data related to outcomes & can also interpret it. So on this new & diverse data one can predict customer behaviors in a better manner. It facilitates accurate Medical Predictions and Diagnosis. ML helps to easily find critical patients, to make perfect diagnoses it also suggests best medicines and to predict readmission. In this way it helps medical sector to cure patients’ health in affordable costs.