Topics Sub-topics Resources
Probability and Statistics fundamentals Probability cheatsheet https://drive.google.com/drive/folders/1tlhghbMC4IziXQw2Y4XMv76DOuBWgBd2
Article on mean, variance and standard deviation https://www.geeksforgeeks.org/mathematics-mean-variance-and-standard-deviation/
Python Basics of python https://www.youtube.com/watch?v=rfscVS0vtbw(uptil 2hrs, 20mins)
https://www.w3schools.com/python/python_intro.asp
Numpy library https://www.youtube.com/watch?v=QUT1VHiLmmI
Pandas library https://www.youtube.com/watch?v=5JnMutdy6Fw
Quiz on Pandas(w3schools) https://www.w3schools.com/python/pandas/pandas_quiz.asp
Data Visualization using Matplotlib https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB
(refer videos 1-7)
Machine Learning Introduction to Machine Learning https://www.geeksforgeeks.org/introduction-machine-learning/?ref=lbp(article)
Introduction to data in Machine Learning https://www.geeksforgeeks.org/ml-introduction-data-machine-learning/?ref=lbp
Fundamental topics: Cross validation, Confusion Matrix, Sensitivity and Specificity, Bias and Variance, ROC and AUC https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=1
(Watch videos 1-7)
Linear Regression and Linear Models https://www.youtube.com/watch?v=PaFPbb66DxQ&list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU
(entire playlist)
Blog: Python implementation of linear regression
(geeksforgeeks https://www.geeksforgeeks.org/linear-regression-python-implementation/
(geeksforgeeks article)
Blog: Introduction to machine learning algorithms: Linear Regression
(medium) https://medium.com/towards-data-science/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a
Blog: Simple and Multiple Linear Regression in Python
(medium) https://medium.com/towards-data-science/simple-and-multiple-linear-regression-in-python-c928425168f9
Logistic Regression https://www.youtube.com/watch?v=C4N3_XJJ-jU&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe&index=7
(entire playlist)
Linear vs Logistic Regression https://www.youtube.com/watch?v=OCwZyYH14uw
Blog: Introduction to Logistic Regression https://medium.com/towards-data-science/introduction-to-logistic-regression-66248243c148
Regularization(Ridge and Lasso regression) https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=18
(watch videos 25-29)
Principle Component Analysis(PCA) https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=18
(watch 30, 31, 32, 33, 34)
Hierarchical Clustering, K-means clustering, K-Nearest Neighbors https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=18
(videos 40, 41, 43)
Support Vector Machines https://www.youtube.com/watch?v=efR1C6CvhmE&list=PLblh5JKOoLUL3IJ4-yor0HzkqDQ3JmJkc
(entire playlist)
https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
(reading material)
https://scikit-learn.org/stable/modules/svm.html
(reading material)
Random Forest Algorithm https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&list=PLblh5JKOoLUIE96dI3U7oxHaCAbZgfhHk
(entire playlist)
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
(reading material)
https://builtin.com/data-science/random-forest-algorithm(short blog)
Gradient Boost https://www.youtube.com/watch?v=3CC4N4z3GJc&list=PLblh5JKOoLUJjeXUvUE0maghNuY2_5fY6
(entire playlist)
DSA https://anubhavsinha98.medium.com/resources-to-master-data-structures-and-algorithms-24450dc6d52b
Assorted list of DSA problems from Leetcode
(recommended to practice everyday) https://seanprashad.com/leetcode-patterns/
SQL
(needed if targeting banks) https://www.w3schools.com/sql/default.asp
https://www.youtube.com/watch?v=7S_tz1z_5bA
Introduction to neural networks
(deep learning is important only if mentioned in resume) https://medium.com/technologymadeeasy/for-dummies-the-introduction-to-neural-networks-we-all-need-c50f6012d5eb
https://www.youtube.com/watch?v=vpOLiDyhNUA
Object Oriented Programming (optional) https://www.geeksforgeeks.org/object-oriented-programming-in-cpp/

Additional resources:

| Puzzle Practice | https://www.ted.com/search?q=ted+ed+riddles (Ted-ed riddles) | | --- | --- | | | https://www.geeksforgeeks.org/top-20-puzzles-commonly-asked-during-sde-interviews/ (geeksforgeeks article) | | Visual Guide to various ML concepts | https://www.youtube.com/playlist?list=PLRZZr7RFUUmXfON6dvwtkaaqf9oV_C1LF | | Case Studies(only specific to some companies like AmEx) | https://drive.google.com/drive/folders/1tlhghbMC4IziXQw2Y4XMv76DOuBWgBd2(spg-practise-case) | | Basics of probability and statistics (playlist of lectures, optional) | https://www.youtube.com/watch?v=qBigTkBLU6g&list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9&index=1 (Watch videos 1-22) | | Problem practice resource from InterviewBit website(for practicing puzzles, programming, and data science) | https://www.interviewbit.com/practice/ | | ML Cheatsheet | https://drive.google.com/drive/folders/1tlhghbMC4IziXQw2Y4XMv76DOuBWgBd2 | | Python Cheatsheet | https://drive.google.com/drive/folders/1tlhghbMC4IziXQw2Y4XMv76DOuBWg |

The link to the document which consists of all these resources is given below:

https://docs.google.com/document/d/1qtC_lpli9DV-mTrVtA_4tAEMz1B1jcpFImfqZJb1il0/edit