Machine Learning Interview Questions with Explanation Part 2

If you have not found the previous part of this tutorial then I would recommend you to visit Machine Learning Interview questions with an explanation post before you understand the below questions.

11 What is Heteroscendasticity?

12. What is Histogram?

13. What is Objective Function?

14. What is PCA?

15. What is the T Test?

16. What is SMOTE?

Comments

Improve Your Computer’s Technology And Expand Your Company!

The world today has become a world run by machines and technologies. There is almost no human on Earth who can complete his or her work or do any job without using a type of device. We need the help of computers and laptops for our daily professional practice and career, and we use the laptop or computer systems for even playing games or to communicate with our extended family members. We are so dependent on our computers and mobile phones that any improvement in either one’s technological features makes us upgrade to the newest version. With this increased dependency, the new way of making the computer systems and other machines fully capable of keeping up with our demands, we have needed to make the tools to work and complete tasks independently, without human intervention. The invention and introduction of Artificial Intelligence have dramatically helped us to make our machines work better, and with their self-learning techniques, the devices are now able to think about ...

AI in invoice receipt processing

Artificial Intelligence (AI) is improving our lives, making everything more intelligent, better, and faster. Yet, has the Artificial Intelligence class module disturbed your records payable cycles? Indeed, without a doubt !! Robotized Invoice handling utilizing Artificial Intelligence training is an exceptionally entrancing region in the records payable cycle with critical advantages. Artificial Intelligence Course Introduction. Current Challenges in Invoice Processing Numerous receipt information directs driving toward blunders: Large associations get solicitations from different providers through various channels such as organized XML archives from Electronic Data Interchange (EDI), PDFs, and picture records through email, and progressively seldom as printed copy reports. It requires a ton of investment and manual work to have this large number of various sorts of solicitations into the bound-together framework. The blunder-inclined information passage occurring toward the beginni...

How Do You Apply the Concept of Bagging in Machine Learning?

Machine learning has transformed the way we approach data analysis, making it possible to derive insights and predictions from vast amounts of data. Among the various techniques in machine learning, bagging (Bootstrap Aggregating) stands out as a powerful method for enhancing model performance and stability. In this blog post, we will explore the concept of bagging, its applications, and how you can learn more about it through various educational resources. Understanding Bagging in Machine Learning Bagging is an ensemble learning technique designed to improve the accuracy and robustness of machine learning models. It works by generating multiple subsets of the training data through random sampling with replacement. Each subset is then used to train a separate model, and the final prediction is obtained by averaging the predictions from all models (for regression tasks) or by majority voting (for classification tasks). The primary goal of bagging is to reduce variance and minimize the ...

Machine Learning 101

Search This Blog