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Training Your Systems with Python Statistical Modeling [Video]

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Training Your Systems with Python Statistical Modeling [Video]

Curtis Miller

Learn statistical analysis by using various machine learning models

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Video Details

ISBN 139781788293402
Course Length4 hours and 5 minutes

Video Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. This course takes you through the various different concepts that get you acquainted and working with the different aspects of Machine Learning.

You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics with Pandas. From there, you will be introduced to supervised learning, where you will explore the principles of machine learning and train different machine learning models. Next, you’ll work with binary prediction models, such as data classification using K-nearest neighbors, decision trees, and random forests.

After that, you’ll work with algorithms for regression analysis, and employ different types of regression, such as ridge and lasso regression, and spline interpolation using SciPy. Then, you’ll work on neural networks, train them, and employ regression on neural networks. You’ll be introduced to clustering, and learn to evaluate cluster model results, as well as employ different clustering types such as hierarchical and spectral clustering. Finally, you’ll learn about the dimensionality reduction concepts such as principal component analysis and low dimension representation.

Style and Approach

This course balances in-depth content with tutorials that put the theory into practice. This course will give you both a theoretical understanding and practical examples that show you the art of statistical modeling and training with the help of Python’s various tools and packages.

Table of Contents

Classical Statistical Analysis
The Course Overview
Computing Descriptive Statistics with Pandas
Confidence Intervals and Classical Hypothesis Testing –Proportions
Confidence Intervals and Classical Hypothesis Testing – Mean
Diving into Bayesian Analysis
Bayesian Posterior Analysis –Proportions
Bayesian Posterior Analysis –Mean
Finding Correlations Using Pandas and SciPy
Introduction to Supervised Learning
Exploring Various Machine Learning Principles
Training Machine Learning Models
Evaluating Model Results
Binary Prediction Models
Classifying Data in Python Using the k-Nearest Neighbors (KNN)
Working with Decision Trees
Machine Learning Using Random Forests
Making Predictions Using the Naive Bayes Algorithm
Working with Support Vector Machines (SVM) for Classification and Detection
Logistic Regression with Machine Learning
Going Beyond Binary
Regression Analysis and How to Use It?
Linear Models and OLS
Evaluating a Linear Model
Exploring the Bayesian Perspective of Linear Models
Employing Ridge Regression
Employing LASSO Regression
Spline Interpolation Using SciPy
Thinking Machines – Neural Networks
The Perceptron
Neural Network Model
Training a Neural Network
Regression with Neural Networks
Clustering
Diving into Clustering and Unsupervised Learning
k-Means Clustering
Evaluating Clustering Model Results
Hierarchical Clustering
Spectral Clustering
Dimensionality Reduction and How It’s Done?
Objective of Dimensionality Reduction
Principal Component Analysis (PCA)
SVD
Low-Dimensional Representation

What You Will Learn

  • Find correlations in your data using SciPy
  • Train different machine learning models and evaluate their results
  • Make predictions using Naïve Bayes Algorithm with the help of Python code
  • Employ support vector machines for classification and detection
  • Employ ridge and lasso regression models
  • Train a neural network

Authors

Table of Contents

Classical Statistical Analysis
The Course Overview
Computing Descriptive Statistics with Pandas
Confidence Intervals and Classical Hypothesis Testing –Proportions
Confidence Intervals and Classical Hypothesis Testing – Mean
Diving into Bayesian Analysis
Bayesian Posterior Analysis –Proportions
Bayesian Posterior Analysis –Mean
Finding Correlations Using Pandas and SciPy
Introduction to Supervised Learning
Exploring Various Machine Learning Principles
Training Machine Learning Models
Evaluating Model Results
Binary Prediction Models
Classifying Data in Python Using the k-Nearest Neighbors (KNN)
Working with Decision Trees
Machine Learning Using Random Forests
Making Predictions Using the Naive Bayes Algorithm
Working with Support Vector Machines (SVM) for Classification and Detection
Logistic Regression with Machine Learning
Going Beyond Binary
Regression Analysis and How to Use It?
Linear Models and OLS
Evaluating a Linear Model
Exploring the Bayesian Perspective of Linear Models
Employing Ridge Regression
Employing LASSO Regression
Spline Interpolation Using SciPy
Thinking Machines – Neural Networks
The Perceptron
Neural Network Model
Training a Neural Network
Regression with Neural Networks
Clustering
Diving into Clustering and Unsupervised Learning
k-Means Clustering
Evaluating Clustering Model Results
Hierarchical Clustering
Spectral Clustering
Dimensionality Reduction and How It’s Done?
Objective of Dimensionality Reduction
Principal Component Analysis (PCA)
SVD
Low-Dimensional Representation

Video Details

ISBN 139781788293402
Course Length4 hours and 5 minutes
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