Description
Machine Learning with Python: A Mathematical Perspective course. 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 – A Glimpse of the Early History of Machine Learning, Implementation of Perception Learning Algorithms in Python, Adaptive Linear Neurons and Convergence Learning
- A tour of machine learning classifiers using scikit-learn, choosing a classification algorithm, first steps with scikit-learn – perceptron training, modeling class probabilities via logistic regression, maximum margin classification with support vector machines, solving non-linear problems using kernel SVM , decision tree learning, K-nearest neighbor – a lazy learning algorithm.
- Data pre-processing, meta-parameter tuning: building good training sets, dealing with missing data, handling classified data, splitting a dataset into separate training and test sets, bringing features to the same scale, feature selection significant features, feature importance evaluation with random forests, data compression through dimensionality reduction, unsupervised dimensionality reduction through principal component analysis, supervised data compression through linear discriminant analysis, using analysis and Kernel principal component analysis for nonlinear mapping, learning best practices for model evaluation and metaparameter tuning, simplifying workflows with pipelines, using k-fold validation to evaluate model performance
- Regression analysis: prediction of continuous target variables, introduction of linear regression, review of housing data set, implementation of ordinary least squares linear regression model, fitting of robust regression model using RANSAC, performance evaluation of linear regression models, use of adjusted methods for regression of linear regression model to Curve – polynomial regression
- Dealing with non-linear 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 regions High density through DBSCAN
- Multi-Layer Artificial Neural Network and Deep Learning: Modeling Complex Functions with Artificial Neural Networks, Handwritten Digit Classification, Artificial Neural Network Training, About Convergence in Neural Networks, Last Words About 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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Different 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
- Beginner Python developers are curious about machine learning and mathematical modeling
Specifications of the course Machine Learning with Python: A Mathematical Perspective
- Publisher: Yudmi
- teacher: Dr Amol Prakash Bhagat
- Training level: beginner to advanced
- Training duration: 21 hours and 18 minutes
- Number of courses: 42
Course headings Machine Learning with Python: A Mathematical Perspective
Prerequisites of the course Machine Learning with Python: A Mathematical Perspective
- No programming experience needed. You will learn everything you need to know
Course images
Sample video of the course
Installation guide
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Subtitle: None
Quality: 720p
download link
File(s) password: www.downloadly.ir
Volume
5.61 GB
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