A person must be aware of the different type of machine learning (like Supervised, Unsupervised and Reinforcement learning) and what is the basic difference between them. After that a person must know the basic concepts of the ML like: Cost function, Gradient Descent, Residuals, etc. and some important libraries of Python (Numpy, Pandas, Matplotlib, Sklearn).
After having a well knowledge about basic concepts, a person must know about different types of ML algorithms that are important for the interview point of view.
Here are some algorithms:
1. Supervised Learning
i. Linear Regression
ii. Decision Tree
iii. Random Forest
iv. Naive Bayes
v. KNN
vi. SVM
vii. Logistic Regression
viii. Ridge and Lasso
2. Unsupervised Learning
i. K-Means
ii. Fuzzy C-Means
iii. DBSCAN
After having a go through with the above things, anyone can move for the Feature Engineering, and Ensemble Learning part which is used to increase the accuracy of the model that we train. In addition to this, Time-Series Forecasting will also be beneficial for the person who wants to learn more deeply about ML.
Machine Learning is a subset of Data Science and even Machine Learning is applied in every possible area that's why people are also eager to have Machine Learning certification
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