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)