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DataScience and Machine-Learning Roadmap 😎

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Define Goal : PRODUCTS or ALGORITHMS

RESOURCES

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 :

Mathematics-1

Mathematics-2

Mathematics-2

Machine Learning Course by Google

Python Basics

Stanford Course by Andrew ng

Made With ML

Data Preprocessing

Scikit Learn

Tensorflow

Kaggle