Generally, Machine Learning (or Deep Learning) projects are quite unique and also different from traditional web application projects due to the inherent complexity involved with them.
The goal of this article is, not to go through full project management life cycle, but to discuss a few complexities and finer points which may impact different project management phases and aspects of a Machine Learning(or Deep Learning) project, and, which should be taken care of, to avoid any surprises later.
Below is a quick ready reckoner for the topics that we will be discussing in this article.
Let us say, you have trained, fine-tuned and tested Machine Learning(ML) model – sgd_clf, which was trained and tested using SGD Classifier on MNIST dataset. And now you want to deploy it in production, so that consumers of this model could use it. What are different options you have to deploy your ML model in production?
Mathematics is the science which deals with the logic of quantity, shape, and arrangement. Undeniably, math is all around us, in fact in everything we do. It wouldn’t be wrong to say, math is the building block for everything in our daily life period. Money, sports, architecture (ancient and modern), television, mobile devices, and even art, all of it has some mathematical concepts involved in it.
In India, mathematics has its origins in Vedic literature which is nearly four thousand years old. It should come as no surprise that the concept of number ‘0’ was discovered in India; also, various treatises on mathematics were authored by Indian mathematicians. The techniques of trigonometry, algebra, algorithm, square root, cube root, negative numbers, and the most significant decimal system are concepts which were discovered by Indian mathematician from ancient India and are employed worldwide even today.
One of the classic problem that has been used in the Machine Learning world for quite sometime is the MNIST problem. The objective is to identify the digit based on image. But MNIST is not very great problem because we come up with great accuracy even if we are looking at few pixels in the image. So, another common example problem against which we test algorithms is Fashion-MNIST.
Fashion-MNIST is a dataset of Zalando’s fashion article images —consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each instance is a 28×28 grayscale image, associated with a label.
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.
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.
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)
[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]
What is Machine Learning?
Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.
Types of Machine Learning
Machine learning is broadly categorized into three buckets:
Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
Machine Learning Applications
Recommend Friends, Dates, Products to end-user.
Classify content into pre-defined groups.
Find Similar content based on Object Properties.
Identify key topics in large Collections of Text.
Detect Anomalies within given data.
Ranking Search Results with User Feedback Learning.
Classifying DNA sequences.
Sentiment Analysis/ Opinion Mining
Natural Language Processing,
Speech and HandWriting Recognition.
Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.
Introduction to Machine Learning and Mahout
Machine Learning- Types
Machine Learning- Applications
Machine Learning- Tools
Mahout – Recommendation Example
Mahout – Use Cases
Mahout Live Example
Mahout – Other Recommender Algos
Machine Learning with Mahout Presentation
Machine Learning with Mahout Videohttps://www.youtube.com/embed/PZsTLIlSZhI