Usually, the learners from our classes schedule 1-on-1 discussions with the mentors to clarify their doubts. So, thought of sharing the video of one of these 1-on-1 discussions that one of our CloudxLab learner – Leo – had with Sandeep last week.
Below are the questions from the same discussion.
You can go through the detailed discussion which happened around these questions, in the attached video below.
You already know that artificial
intelligence is grabbing the world, transforming nearly every industry,
business, trade, and function. But what you might not know are the incredible
AI technologist and researchers powering the edge of this momentous revolution.
Breakthroughs in AI are incredible, isn’t
it? But how does it all occur? It’s when extremely talented and diverse
thinkers from unique backgrounds, disciplines, expertise, and perspectives come
forward. You might think of names like Andrew Ng (Baidu), Amit Singhal (Uber), Elon
Musk (SpaceX & Tesla), Ginni Rometty (IBM) and Ray Kurzweil (Google) – but
wait why all men?
2019 March, on International Women’s Day, CloudXLab aims to showcase
11 incredible women in AI.
In recent years, career opportunities in artificial
intelligence (AI) has grown exponentially to meet the rising demand of
digitally transformed industries. But while there are amply of jobs available
in AI, there’s a momentous shortage of top talent with the essential skills.
But why does this demand and supply gap exist? Many aspiring candidates wish to
join the AI bandwagon, but several myths hold them behind.
Job site Indeed
highlights, the demand for AI skills has doubled over the last 3 years,
and the total number of job postings has upsurged by 119 percent. But,
job-aspirants interest in a career in artificial intelligence seems to have
leveled off. This clearly indicates employers are struggling to get good
talent. This is surely good news for all those planning to transit their
careers into AI!
Well, building a career in artificial intelligence demands a self-controlled approach. You might be interested in a career switch because of the exciting opportunities floating in this booming industry or maybe for that long deep-rooted interest to pursue AI as a career. Regardless of what your inspiration is, the first step to moving ahead in the AI career path is to ditch the myths and misconceptions that for long has been blocking your path.
Say you come up with a wonderful idea such as a really great phone service. You would want this phone service to be available to the APIs in various languages. Whether people are using Python, C++, Java or any other programming language, the users should be able to use your service. Also, you would want the users to be able to access globally. In such scenarios, you should create the Thrift Service. Thrift lets you create a generic interface which can be implemented on the server. The clients of this generic interface can be automatically generated in all kinds of languages.
Let us get started! Here we are going to create a very simple service that just prints the server time.
What computing did to the usual industry earlier, Machine Learning is doing the same to usual rule-based computing now. It is eating the market of the same. Earlier, in organizations, there used to be separate groups for Image Processing, Audio Processing, Analytics and Predictions. Now, these groups are merged because machine learning is basically overlapping with every domain of computing. Let us discuss how machine learning is impacting e-commerce in particular.
The first use case of Machine Learning that became really popular was Amazon Recommendations. Afterwards, the Netflix launched a challenge of Movie Recommendations which gave birth to Kaggle, now an online platform of various machine learning challenges.
Before I dive deep into the details further, lets quickly brief the terms that are found often confusing. AI stands for Artificial Intelligence which means being able to display human-like intelligence. AI is basically an objective. Machine learning is making computers learn based on historical or empirical data instead of explicitly writing the rules. Artificial Neural networks are the computing constructs designed on a similar structure like the animal brain. Deep Learning is a branch of machine learning where we use a complex Artificial Neural network for predictions.
These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.
For hiring machine learning engineers or data scientists, the typical process has multiple rounds.
A basic screening round – The objective is to check the minimum fitness in this round.
Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
Bar Raiser / Hiring Manager – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.
At CloudxLab, we have always believed in quality education must be affordable for everyone so that we can help learners achieving career goals and build innovative products.
If you can’t afford to pay for a course, you can apply for financial aid using this form. Learners with Financial Aid in a course will be able to access all of the course content and complete all work required to earn a certificate. Financial Aid only applies to the course that the Financial Aid application was approved for. Most courses offer Financial Aid, but Financial Aid may not be available for certain courses. It will take a minimum of 7 days for us to review your financial aid application. When your application is reviewed, you’ll get an email letting you know whether it’s been approved or denied.
Recently, a friend whose company is working on large scale project reached out to us to seek a solution to a simple problem of finding a list of phrases (approximately 80,000) in a huge set of rich text documents (approx 6 million).
The problem at first looked simple. The way engineers had solved it is by simply loading the two documents in Apache Spark’s DataFrame and joining those using “like”. Something on these lines:
select phrase.id, docs.id from phrases, docs where docs.txt like ‘%’ + phrases.phrase + ‘%’
But it was taking huge time even on the small subset of the data and processing is done in distributed fashion. Any Guesses, why?
They had also tried to use Apache Spark’s broadcast mechanism on the smaller dataset but still, it was taking a long while finishing even a small task.
After receiving a huge response in our last scholarship test, we are once again back with a basic conceptual test to attain scholarship for our upcoming Specialization course on Machine Learning and Deep Learning.
Concepts to be tested: Linear algebra, probability theory, statistics, multivariable calculus, algorithms and complexity, aptitude and Data Interpretation.
Date and Time: September 2, 2018, 8:00 am PDT (8:30 pm IST)