Machine learning/Data science
Internship


8k-10k stipend


Apply now

https://angel.co/company/litifer/jobs

TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming


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👩🏻‍💻 Why should one study Linear Algebra for ML?

👉🏼 Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.

👉🏼 Learn Linear Algebra for Machine Learning with:

Khan Academy: https://www.khanacademy.org/math/linear-algebra

Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning

Here are some amazing freely available ebooks on the same topic:

Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf

An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/

Happy machine learning! 🎉