Every day the world is advancing into the new level of industrialization and this has resulted in the production of a vast amount of data. And, at initial stages, people started considering it as a bane, but later they found out that it’s a boon. So, they started using this data in a productive way. Big data and machine learning are terminologies based on the concept of analyzing and using the same data. Let’s get into more details.
Continue reading “Big Data vs Machine Learning”Category: Machine Learning
Things to Consider While Managing Machine Learning Projects
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.
Continue reading “Things to Consider While Managing Machine Learning Projects”Deploying Machine Learning model in production
In this article, I am going to explain steps to deploy a trained and tested Machine Learning model in production environment.
Though, this article talks about Machine Learning model, the same steps apply to Deep Learning model too.
Below is a typical setup for deployment of a Machine Learning model, details of which we will be discussing in this article.
The complete code for creating a REST service for your Machine Learning model can be found at the below link:
https://github.com/cloudxlab/ml/tree/master/projects/deploy_mnist
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?
Continue reading “Deploying Machine Learning model in production”9 Indian Mathematicians Who Transformed The Norms Of Knowledge- Now It’s On Us
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.
Continue reading “9 Indian Mathematicians Who Transformed The Norms Of Knowledge- Now It’s On Us”Fashion-MNIST using Machine Learning
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.
The complete code for this project you can find here : https://github.com/cloudxlab/ml/tree/master/projects/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.
Continue reading “Fashion-MNIST using Machine Learning”One-on-one discussion on Gradient Descent
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.
Use-cases of Machine Learning in E-Commerce
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.
Continue reading “Use-cases of Machine Learning in E-Commerce”
Top Machine Learning Interview Questions for 2019 (Part-1)
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.
Continue reading “Top Machine Learning Interview Questions for 2019 (Part-1)”
Scholarship Test for Machine Learning Course
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)
- Type: objective (MCQ)
- Number of questions: 25
- Duration: 90 minutes
- Mode: Online
If you have a good aptitude and general problem-solving skills, this test is for you. So, go ahead and earn what you deserve.
If you have any questions on the test or if anything else comes up, just click here to let us know. We’re always happy to help.
How To Optimise A Neural Network?
When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.
Is your model underfitting or overfitting?
You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.
You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.
Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.
If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.