machinelearningmindset/machine-learning-course
Machine Learning Course
This project is a comprehensive educational curriculum for learning data science and predictive modeling using the Python programming language. It provides structured instructional material and guides covering supervised learning, unsupervised learning, and neural network design.
The curriculum focuses on building, training, and evaluating machine learning models. It includes specific guides for implementing linear regression, decision trees, and support vector machines for predictive analysis, as well as tutorials on designing convolutional and recurrent neural network architectures.
The course covers a broad range of data science capabilities, including model performance evaluation through cross-validation, the discovery of hidden patterns using clustering and principal component analysis, and the development of deep learning models using layered computational graphs.
Learning is delivered through a notebook-based interactive format that combines executable code with descriptive text.
Features
- Predictive Model Development - Offers a comprehensive guide to the process of designing, training, and testing predictive machine learning models.
- Model Performance Evaluators - Provides tools and methods for quantifying predictive accuracy and establishing performance baselines.
- Neural Network Architectures - Provides instructional material on the design and function of convolutional and recurrent neural networks.
- Unsupervised Learning - Teaches how to discover hidden structures in unlabeled data using clustering and principal component analysis.
- Python Data Science Courses - Offers a comprehensive Python-based educational curriculum covering data science, predictive modeling, and ML frameworks.
- Interactive Notebook Learning Resources - Delivers educational content through interactive Jupyter notebooks combining executable code cells with descriptive text.
- Predictive Modeling Curricula - Provides a structured learning path for building, training, and evaluating machine learning models.
- Linear Regression - Covers the fundamental use of linear regression to establish performance baselines for predictive modeling.
- Machine Learning Implementations - Provides code-based implementations of decision trees and support vector machines for mapping input data to targets.
- Hybrid Convolutional Recurrent Networks - Covers the design of hybrid networks combining convolutional layers for feature extraction and recurrent layers for temporal dependencies.
- Neural Networks - Teaches the design of multi-layered computational architectures including convolutional and recurrent networks.
- K-Fold Cross-Validation - Implements K-fold cross-validation pipelines to ensure model generalisation and prevent overfitting.
- TensorFlow Model Definitions - Teaches how to build deep learning models using TensorFlow's layered computational graphs.
- Dimensionality Reduction - Implements principal component analysis to reduce high-dimensional data into lower-dimensional representations.
- Unsupervised Learning - Provides educational content on identifying patterns in unlabeled data through clustering and PCA.
- Neural Network Tutorials - Provides tutorials on implementing convolutional and recurrent architectures for processing complex data patterns.
- Machine Learning Guides - Provides guides for applying Python to implement linear regression, decision trees, and support vector machines.
- Scikit-Learn Examples - Provides practical code demonstrations using Scikit-Learn for implementing decision trees and support vector machines.
- Learning and Reference - ML course with Python.
- Machine Learning Algorithms - Structured course material covering essential machine learning concepts.
- Interview Preparation - A structured course for learning machine learning concepts.
Star history
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