Welcome to my machine learning journey repository! Here, I document my progress and learnings as I dive into the fascinating world of machine learning.
In this repository, you'll find various code files and Jupyter Notebooks that reflect my learning process. Each file corresponds to a different aspect of machine learning, covering topics such as data preprocessing, model selection, evaluation, and experimentation.
- Notebooks: This directory contains Jupyter Notebooks where I explore different machine learning concepts and algorithms.
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1. Exploring Data Preprocessing: This notebook covers the essentials of data preprocessing, including handling missing values, encoding categorical variables, and scaling features.
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2. Model Selection and Evaluation: Here, I delve into the process of selecting the right machine learning model for a given problem and evaluating its performance using various metrics.
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3. Hyperparameter Tuning: In this notebook, I explore the concept of hyperparameter tuning to optimize the performance of machine learning models.
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4. Building Pipelines: Learn how to streamline the machine learning workflow using Scikit-Learn's Pipeline class.
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5. Experimentation and Iteration: Discover the importance of experimentation in machine learning and how to iteratively improve model performance.
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6. Conclusion and Next Steps: A concluding notebook where I summarize my learning journey and suggest resources for further exploration.
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Scikit-Learn User Guide: Explore more about the Scikit-Learn library and discover new features and techniques.
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Kaggle: Participate in Kaggle competitions to practice your machine learning skills and compare results with others.
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CatBoost Library: Dive into CatBoost, an advanced version of a decision tree algorithm, used in production at several large technology companies.
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: A highly recommended book by Aurélien Géron for practical examples and in-depth knowledge.
Join me on my machine learning journey as I explore, experiment, and learn new concepts and techniques. Together, let's unlock the mysteries of machine learning and harness its power to solve real-world problems!