Linear Algebra, Probability, Basic optimization techniques like gradient descent, most commonly used machine learning models like Naive Bayes, Linear Regression, Logistic Regression, Support Vector Machines, Neural Networks, dimensionality reduction, and k-means Clustering. Practical experience of some of these will help.
Knowledge of bias-variance trade-off, validation procedures, common performance metrics such as accuracy, precision-recall, etc. is also really important.
Programming knowledge of one scripting language and one object-oriented language is a must.
While equipping yourself with above, you will naturally end up knowing one or more Machine Learning packages, which is also a must.
You can refer the following article which is based on Machine Learning Interview Questions, which can help you crack the interview:
And if you are more into videos then refer the following video which is based on Interview questions on Data Science:
If you are a beginner and want to know more about Machine Learning, then check out this course by Intellipaat which will teach you ML from basics: Machine Learning Course
And if you are more into youtube tutorials then here is an awesome video tutorial by Intellipaat which will clear all your doubts regarding the same:
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