Interview Tips

"A good company understands that problem solving ability and the ability to learn new things are far more important than knowledge of a specific programming language or web framework and that smart students can pick-up any new skill in almost no time."

"With the rise of the tech startup ecosystem in India, the number of tech jobs and internships are at the peak. Today, a large number of companies hire fresh graduates from college because college freshers bring fresh thinking, they are quite agile and they can easily be moulded into any domain."

"There are many Backend Development frameworks in multiple programming languages. The choice of the Backend framework usually is driven by your proficiency in the underlying programming language.

1. Java: Spring
2. Python: Django
3. JavaScript: NodeJS
4. Ruby: Ruby-on-Rails (RoR)
5. PHP: Codeigniter

So, if you are proficient in say JavaScript, you can start with NodeJS or any other related JavaScript framework. Other than the underlying programming language, the differences between these frameworks aren’t really significant at the beginner level. Note that for advanced features, the frameworks may differ completely. However, for beginners, either is fine."

"If you are aiming for say, Android app developer internship, you should certainly try and get some projects on your resume. Many students worry about the “certification” of the project. The fact is that the interviewer doesn’t really care about the “certification”. The fact that you took an initiative to learn Android app development and you implemented a project, in itself tells the interviewer about your enthusiasm and learning capabilities. If they want to further verify it, they will question you about the details of the project which will make it clear to them if you are bluffing."

"Obviously, a good GPA/CPI also helps a lot. However, don’t worry if your GPA is low. You can always make up for it through some great projects that align well with what the company wants."

"Machine Learning, the most acknowledged skill in today's tech world."

 "Machine Learning is 65% maths, 25% Algorithms designing and 10% data preprocessing , therefore you should be a champion of 

1.Linear Algebra
2.Calculus
3.Probability  and Statistics

next important things are

4.Programming
5. Algorithms"

 "Why Maths? Maths is needed to understand the machine Learning algorithms/models or to implement new ones. There is a large number of models(algorithms) which are already built. Even when you are using existing models you need to understand the internal working of the algorithm so that the hyperparameters can be tuned. 

A single model may not give the best results for all the problems. Identifying which model to use for a given problem is very important and to choose the right model, you need to understand the internal working/maths."

Data Structure & Algorithms (non-ml): Though this part will not help you directly rather it enhances your thinking and logic designing which is helpful in designing new ML algorithms and in understanding concepts like:

1.Time Complexity

2.Space Complexity

3.Sorting and Searching

4.Shortest Path between two Points

5. Problem Solving approaches like Greedy, Dynamic, etc.

For Data structure follow mycodeschool playlist on Youtube.

You can implement the teachings in python by following Nptel videos.

For  Algorithms part, you can go through Algorithms playlist on Youtube (This is a really cool playlist on youtube, covering almost all topics) and for advanced algorithms you can refer : Algorithms 1 and Algorithms 2  playlists by Stanford Algorithms (I would suggest you to go through both the playlists).

Machine Learning (Algorithms and Implementation) (about 5-6 months): Now here comes the most awaited part, so let's start to get into actual ML. Machine Learning course by Coursera is highly recommended worldwide for ML learners (Most fundamental and comprehensive course anyone ever came across).

Machine Learning Future Scope (Higher Studies and Research):

Today, with a high demand for ML skills, many post-graduate programs, diplomas and research programs have been introduced all over the world which promises a successful career in this field. Some of the diploma courses  which are highly anticipated by many people are:

PG program in Machine Learning and AI from IIIT-B by UpGrad: This course focuses on statistics essentials such as using statistics to describe data and infer insights, building machine learning models using supervised, unsupervised learning, natural language processing, neural networks, deep learning, graphical models, reinforcement learning etc. In addition to these, students get a chance to work on cutting-edge projects such as predicting customer churn in the telecom industry, building a chatbot engine, disease prediction using medical imaging, among several others. You can check it out by clicking here.

Foundations of Machine Learning and AI from IIIT-H by TalentSprint: The program is delivered using five different components — classroom lectures, where they learn concepts; labs which are done on the cloud; mentors; industry workshops and hackathons. As a part of industry workshop, senior technical heads from top tech companies share their experience and insights on using and implementing AI. Some of them are Ranga Pothula (President, HYSEA; VP and Centre Head Infor), Dr Anbumani Subramanian (Lead Architect, Intel Corporation), Dr Shailesh Kumar (Vice President and Distinguished Scientist, Ola), Mithun Das Gupta (Principal Applied Researcher, Microsoft), Sundar Srinivasan (General Manager, Microsoft AI and Research), and others. The curriculum is designed keeping in mind working professionals. You can find more details by clicking here.

Post Graduate Program in Machine Learning and AI by Great Learning: This 12-month blended program builds a solid foundation by covering areas like computer vision, NLP and intelligent virtual agents, among others. This comprehensive program covers a range of topics from traditional supervised and unsupervised learning methods to ensembles. It focuses more on labs, projects and Capstone project building, a robust e-portfolio of work. It has 9 hands-on projects, GPU based lab environment to build deep learning models, guidance from industry experts through workshop session, among others. You can check it out at Greatlearning.

Also, you can check out the post-graduate and Doctorate programs in some renowned institutes of India like IISc Bangalore, IIT Bombay, IIT Delhi, IIT Madras, ISI Kolkata where you can select the programs in Machine Learning and Statistics, you can check the enrollment procedure at their home site with just a simple google search with name of Institute, you can find some of the renowned professors in this field to complete your research under them at Analytics India.

In a nutshell Machine Learning is the new electricity in today's world, it is not limited to what you have learned, rather it's about Development, Improvisation and Application.

Machine Learning (Algorithms and Implementation) (about 5-6 months): Now here comes the most awaited part, so let's start to get into actual ML. Machine Learning course by Coursera is highly recommended worldwide for ML learners (Most fundamental and comprehensive course anyone ever came across).

On completing this course you will be familiar with:

1.Decision Trees

2.Naive Bayes

3.Linear Regression

4.Logistic Regression

5.Support Vector Machines

6.KNN

7.Ensembling

8.Unsupervised Learning

9.Gradient Descent

Disclaimer: This course is taught in Octave/Matlab which might lower your interest, you can implement the course teachings using  numpy, pandas and matplotlib or seaborn (python libraries).

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