Starting a 100 Days Code Challenge for Learning Data Science from Scratch is my goal on Learning Data Science in Machine Learning by:
- Learning Fundamentals of Python
- Python Libraries for Data Science
- Data Manipulation and Preprocessing
- Machine Learning Basics
- Advanced Machine Learning Techniques
- Deep Learning and Neural Networks
- Model Evaluation and Deployment
- Data Science Project and Wrap-Up
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30 β | 31 β |
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30 β |
Section | Description |
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Basic_Python | Covers fundamental syntax, control structures, functions, and core Python concepts |
OOPS | Object-Oriented Programming principles with practical implementations |
Structure | Coverage |
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Array | Includes arrays, lists, strings, tuples, sets, and dictionaries with operations. |
Section | Coverage |
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NumPay | Numerical computing and array operations |
Pandas | Data manipulation and analysis |
Matplotlib | Data visualization and plotting |
Seaborn | Statistical data visualization |
Sk Learn | Machine learning algorithms and models |
Section | Coverage |
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Data Manipulation | feature scaling, encoding categorical data, data normalization preprocessing steps to improve data quality and model performance. |
Topics | Use Case |
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Linear Algebra | Vectors, Matrices |
Statistics & Probability | Mean, Variance, Probability Distributions |
Calculus | Derivatives, Gradients |
Optimization Techniques | Gradient Descent |