DataScience and Machine-Learning Roadmap

This repository contains all the notebooks that were used in the Data Science bootcamp held on 18th-22nd January, 2021

Define Goal : PRODUCTS or ALGORITHMS

Maths

Linear Algebra (Matrix, Vector)
Statistics
Probability

Learn Python & its Libraries

Numpy
Pandas

Learn ML Algorithms

Supervised vs Unsupervised vs Reinforcement
Linear RegressionDefine Goal : PRODUCTS or ALGORITHMS

Maths

Linear Algebra (Matrix, Vector)
Statistics
Probability

Learn Python & its Libraries

Numpy
Pandas

Learn ML Algorithms

Supervised vs Unsupervised vs Reinforcement
Linear Regression, Logistic Regression, Clustering
KNN (K Nearest Neighbours)
SVM (Support Vector Machine)
Decision Trees
Random Forests
Overfitting, Underfitting
Regularization, Gradient Descent, Slope
Confusion Matrix

Data Preprocessing (for higher accuracy)

Handling Null Values
Standardization
Handling Categorical Values
One-Hot Encoding
Feature Scaling

Learn ML libraries

Scikit learn
Matplotlib
Tensorflow for DL

1) Practice and participate in various competetions of kaggle

2) Explore projects on Github

Resources :

http://www.maths.qmul.ac.uk/~pjc/notes/linalg.pdf (Maths)

https://www.mathsbox.org.uk/twi/astats.pdf (Maths)

https://www.youtube.com/playlist?list=PLLy_2iUCG87D1CXFxE-SxCFZUiJzQ3IvE (Maths)

https://developers.google.com/machine-learning/crash-course (ML by Google)

https://www.datacamp.com/courses/intro-to-python-for-data-science (Python Basics)

https://www.coursera.org/learn/machine-learning (Stanford Course by Andrew ng)

https://madewithml.com/

https://www.javatpoint.com/data-preprocessing-machine-learning (Data Preprocessing)

https://scikit-learn.org/stable/ (Scikit Learn)

https://www.tensorflow.org/ (Tensorflow)

https://www.kaggle.com/ (Kaggle)