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DataScience and Machine-Learning Roadmap 😎
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Define Goal : PRODUCTS or ALGORITHMS
RESOURCES
Maths
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Linear Algebra (Matrix, Vector)
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Statistics
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Probability
Learn Python & its Libraries
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Numpy
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Pandas
Learn ML Algorithms
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Supervised vs Unsupervised vs Reinforcement
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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
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Linear Regression, Logistic Regression, Clustering
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KNN (K Nearest Neighbours)
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SVM (Support Vector Machine)
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Decision Trees
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Random Forests
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Overfitting, Underfitting
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Regularization, Gradient Descent, Slope
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Confusion Matrix
Data Preprocessing (for higher accuracy)
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Handling Null Values
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Standardization
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Handling Categorical Values
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One-Hot Encoding
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Feature Scaling
Learn ML libraries
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Scikit learn
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Matplotlib
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Tensorflow for DL
1) Practice and participate in various competetions of kaggle
2) Explore projects on Github
Resources :
Machine Learning Course by Google
