Welcome to my Machine Learning repository! This collection is a comprehensive guide to key Machine Learning concepts, techniques, and practical implementations. I've organized the content into modules, each focusing on different aspects of Machine Learning, from foundational principles to advanced algorithms and projects.
In this section, you'll find an introduction to the world of Machine Learning, including the core concepts that differentiate it from traditional programming. We also dive into the essential steps of data preparation, which is crucial for any ML project.
As we progress, you'll explore various algorithms and techniques for supervised and unsupervised learning. Each module covers theoretical concepts followed by practical implementation examples using Python, with and without the use of popular libraries like sklearn.
This section introduces you to Neural Networks and Convolutional Neural Networks (CNNs). You'll gain insights into how these models work, including hands-on projects like the MNIST Handwriting Recognition, implemented using both Neural Networks and CNNs.
Further modules cover more specialized topics such as overfitting and underfitting, clustering algorithms, and the Apriori algorithm. These sections are designed to deepen your understanding and ability to apply ML techniques to a variety of challenges.
To solidify your learning, I've included several hands-on projects throughout the modules. These projects are designed to bridge the gap between theory and practice, allowing you to apply the concepts learned in real-world scenarios -
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Linear Regression Projects Implementations that range from using libraries to building the models from scratch. These projects help you understand the intuition behind linear regression and its applications.
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Logistic Regression Projects Explore the cost function, and implement logistic regression both with and without libraries. These projects showcase how logistic regression is used in classification problems.
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Naive Bayes and K-Nearest Neighbours Projects Practical implementations of these algorithms to solve classification tasks, providing a deeper understanding of their working mechanisms.
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Decision Tree Projects Includes step-by-step examples and full implementations, helping you grasp decision-making processes in machine learning.
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Clustering Projects Work on projects that implement clustering algorithms like K-Means and Hierarchical Clustering. These projects demonstrate the application of clustering in unsupervised learning.
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Neural Networks and CNN Projects These projects include building and training models for tasks like the MNIST Handwriting Recognition. You'll implement these models using neural networks and CNNs, gaining experience in deep learning.
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Apriori Algorithm Project A project focused on association rule mining using the Apriori algorithm, showcasing its application in discovering relationships between variables in large datasets.
Each project is designed to reinforce the theoretical knowledge covered in the modules, offering a practical perspective on how these algorithms and techniques are used in real-world machine learning tasks.
- Fundamental Machine Learning concepts and the differences from traditional programming.
- Data preprocessing techniques to clean, transform, and reduce data.
- Building and implementing algorithms for both supervised and unsupervised learning.
- Deep Learning basics, including Neural Networks and CNNs, with practical projects.
- Techniques to address common issues like overfitting and underfitting.
- Application of clustering algorithms and association rules in real-world scenarios.
- Hands-on experience through project work, applying learned concepts to solve real problems.
Each module is structured to provide you with both theoretical knowledge and practical coding experience. Begin with the basics and progress through the modules, or jump to a specific topic that interests you. The accompanying code examples and projects are provided to enhance your learning experience.