In this post, you will learn about machine learning and deep learning algorithms and frameworks. Furthermore, you will learn tips and tricks on how to handle the data, select metrics, and improve the model performance. The last and most essential cheat sheet is about machine learning interview questions and answers with visual examples. The Machine Learning Algorithms cheat sheet is all about algorithm's description, applications, advantages, and disadvantages. It is your gateway into the world of supervisor and unsupervised machine learning models, where you will learn about linear and tree-based models, clustering, and association.

In this article of my series "Ace your Machine Learning Interview" I continue to talk about Ensemble Learning and in particular, I will focus on Boosting algorithms with special reference to AdaBoost. I hope that this series in which I review the basics of Machine Learning will be useful to you in facing your next interview! We talked in the last article in general about what Ensemble Learning is and we have seen and implemented simple Ensmble methods based on Majority Voting. Today we talk more in detail about an Ensemble method called Boosting by making special reference to Adaptive Boosting or AdaBoost. You may have heard of this algorithm before, it is often used to win Kaggle competitions for example.

These days I am having several interviews in the field of Machine Learning as I have moved abroad and need to look for a new job. Big companies and small startups always want to make sure you know the fundamentals of Machine Learning, and so I'm using some of my time going over the basics again. So I decided to share a series of articles about what you need to know to deal with interviews in Machine Learning hoping it will help some of you as well. When we talk about Linear Regression, we have a set of points that for ease you can think of plotted on a plane in 2 dimensions (x: feature, y: label) and we want to fit these points with a straight line. That is, we want to find that straight line that passes right'between' the points as in the figure above.

Hello friends, we are here again today for another exciting topic to discuss. But, today we are not gonna discuss something which is related to Java or any other language or spring boot. Today we are gonna discuss something which is immensely practical and has the potential to land you very high-paying data science jobs. Today we are gonna review a course that focuses on Machine Learning! Machine Learning is very important when we are considering data science interviews! It couldn't have come at a better moment, with machine learning expected to be a $3.6 billion business by 2024.

Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you're using. This can lead to the model underfitting your data, making it hard for it to have high predictive accuracy and for you to generalize your knowledge from the training set to the test set. Variance is error due to too much complexity in the learning algorithm you're using. This leads to the algorithm being highly sensitive to high degrees of variation in your training data, which can lead your model to overfit the data. You'll be carrying too much noise from your training data for your model to be very useful for your test data.

NeurIPS 2019 was with around 13,000 attendees the largest ML conference of the year. The NeurIPS 2019 Program Chairs did a fantastic analysis of the reviewing process. NeurIPS has no free-loader problem: Most of the authors of submitted papers participate in reviewing. It is still unclear how to filter papers before the full review. Review quality (as measured by length) is not lower compared to smaller conferences.

As a PhD student, I have a fairly good understanding of ML algorithms but still found machine learning interviews challenging.

Quick tip for those who prepare an interview for an internship or a PhD or a postdoc in the academia: be able to answer some simple linear algebra exercises. Machine learning research is something different from gluing some python code found on stack overflow, and professors are desperate to find people that understand what they are doing.