Description
Machine Learning with Python: A course from a mathematical perspective. Machine Learning: three different types of machine learning, introduction to basic terms and symbols, roadmap for building machine learning systems, using Python for machine learning
- Teaching simple machine learning algorithms for classification, artificial neurons – an insight into the early history of machine learning, implementing perceptual learning algorithms in Python, adaptive linear neurons and convergence learning
- A tour of machine learning classifiers using Scikit-Learn, choosing a classification algorithm, getting started with Scikit-Learn – perceptron training, modeling class probabilities using logistic regression, maximum margin classification using support vector machines, solving nonlinear problems using kernel SVM, decision tree learning, K-nearest neighbor – a lazy learning algorithm.
- Data preprocessing, metaparameter tuning: creating good training sets, dealing with missing data, dealing with classified data, splitting a dataset into separate training and test sets, bringing features to the same scale, selecting important features, evaluating feature importance using Random Forests, data compression using dimensionality reduction, unsupervised dimensionality reduction using principal component analysis, supervised data compression using linear discriminant analysis, using parsing and kernel principal component analysis for nonlinear mapping, learning best practices for model evaluation and metaparameter optimization, simplifying workflows with pipelines, using k-fold validation to evaluate model performance.
- Regression Analysis: Predicting continuous target variables, introducing linear regression, examining the housing dataset, implementing ordinary least squares linear regression model, fitting a robust regression model using RANSAC, evaluating the performance of linear regression models, using adapted methods for regression from linear regression model to curve – polynomial regression
- Dealing with nonlinear relationships using random forests, working with unlabeled data – cluster analysis, grouping objects based on similarity using k-means, organizing clusters as a hierarchical tree, locating high density regions using DBSCAN
- Multilayer Artificial Neural Network and Deep Learning: Modeling Complex Functions with Artificial Neural Networks, Handwritten Digit Classification, Training Artificial Neural Networks, About Convergence in Neural Networks, Final Words on Neural Network Implementation, Network Parallelization Training Neural with Flow Tensor, Flow Tensor and Training Function
What you will learn in the course “Machine Learning with Python: A Mathematical Perspective”
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Concepts, techniques and building blocks of machine learning
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Mathematics for modeling and evaluation
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Various classification and regression algorithms for supervised machine learning
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Different clustering algorithms for unsupervised machine learning
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Concepts of reinforcement learning
This course is suitable for people who
- Beginners in Python are curious about machine learning and mathematical modeling
Specifications of the course Machine Learning with Python: A Mathematical Perspective
- Editor: Udemy
- Lecturer: Dr. Amol Prakash Bhagat
- Training level: beginner to advanced
- Training duration: 21 hours and 18 minutes
- Number of courses: 42
Course Titles Machine Learning with Python: A Mathematical Perspective
Prerequisites for the course Machine Learning with Python: A Mathematical Perspective
- No programming experience required. You will learn everything you need to know
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