The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

DSTar.tar.gz

code-job

r-job

This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

Scikit-learn, or sklearn, is the most useful Python-based machine learning library. It was built on top of SciPy, NumPy, Pandas, Matplotlib, etc. It contains a lot of machine learning algorithms to perform different tasks, such as regression, clustering, classification, and text processing.


Scikit-learn is used by programmers to build models. The library can be installed by using the pip command:


<h2> Features of scikit-learn </h2>

These are some of the features of sklearn:

<li>Supervised learning: Scikit-learn supports linear models, support vector machines, nearest neighbor, Naive Bayes methods for binary and multiclass classifications, and regression tasks. Decision trees and ensemble methods are also provided to perform supervised learning.


<li>Clustering: A good number of clustering methods are available. K-means clustering, DBScan, Gaussian mixtures, spectral clustering, and affinity propagation are available in scikit-learn, all with parameter tuning options.


<li> Dimensionality reduction: Dimension reduction can be done using Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and factor analysis.


<li> Model selection and evaluation: Scikit-learn not only provides model training, but also cross-validation, hyperparameter tuning, model evaluation, and validation curves. These help in creating robust models.


<li> Data pre-processing: Data pre-processing and transforming options are built-in. Users can perform mean and variance scaling, non-linear transformations, normalization, and the encoding of categorical features. No need to write code for these modules.


<li> Datasets: Scikit-learn provides a data set to try and test approaches. The data set library has a good number of open data sets, such as MNIST or Iris, to quickly build and test any model. These datasets are useful for learners looking to try out various models.


<li> Feature selection: Sklearn can select features based on algorithms. Algorithms like Random Forest and Adaboost provide a wrapper in Scikit-learn to perform feature selection.

Learn about the key features of the scikit-learn library.

Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Scikit-learn