O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Training Your Systems with Python Statistical Modeling

Video Description

Learn statistical analysis by using various machine learning models

About This Video

  • Exploring important aspects of statistical modeling using Python
  • Filled with real-world, practical examples that show you how to jump in and start building effective prediction models
  • Covers important concepts such regression analysis and dimensionality reduction with the help of Python

In Detail

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.

Table of Contents

  1. Chapter 1 : Classical Statistical Analysis
    1. The Course Overview 00:07:15
    2. Computing Descriptive Statistics with Pandas 00:08:10
    3. Confidence Intervals and Classical Hypothesis Testing – Proportions 00:08:15
    4. Confidence Intervals and Classical Hypothesis Testing – Mean 00:09:12
    5. Diving into Bayesian Analysis 00:04:41
    6. Bayesian Posterior Analysis – Proportions 00:09:20
    7. Bayesian Posterior Analysis – Mean 00:09:16
    8. Finding Correlations Using Pandas and SciPy 00:07:08
  2. Chapter 2 : Introduction to Supervised Learning
    1. Exploring Various Machine Learning Principles 00:05:46
    2. Training Machine Learning Models 00:09:14
    3. Evaluating Model Results 00:09:14
  3. Chapter 3 : Binary Prediction Models
    1. Classifying Data in Python Using the k-Nearest Neighbors (KNN) 00:09:01
    2. Working with Decision Trees 00:07:45
    3. Machine Learning Using Random Forests 00:05:21
    4. Making Predictions Using the Naive Bayes Algorithm 00:07:15
    5. Working with Support Vector Machines (SVM) for Classification and Detection 00:05:05
    6. Logistic Regression with Machine Learning 00:03:40
    7. Going Beyond Binary 00:07:18
  4. Chapter 4 : Regression Analysis and How to Use It?
    1. Linear Models and OLS 00:06:22
    2. Evaluating a Linear Model 00:08:58
    3. Exploring the Bayesian Perspective of Linear Models 00:07:08
    4. Employing Ridge Regression 00:02:50
    5. Employing LASSO Regression 00:02:44
    6. Spline Interpolation Using SciPy 00:09:12
  5. Chapter 5 : Thinking Machines – Neural Networks
    1. The Perceptron 00:04:39
    2. Neural Network Model 00:03:22
    3. Training a Neural Network 00:06:00
    4. Regression with Neural Networks 00:03:06
  6. Chapter 6 : Clustering
    1. Diving into Clustering and Unsupervised Learning 00:07:50
    2. k-Means Clustering 00:07:53
    3. Evaluating Clustering Model Results 00:07:20
    4. Hierarchical Clustering 00:09:03
    5. Spectral Clustering 00:05:10
  7. Chapter 7 : Dimensionality Reduction and How It’s Done?
    1. Objective of Dimensionality Reduction 00:02:37
    2. Principal Component Analysis (PCA) 00:03:36
    3. SVD 00:08:01
    4. Low-Dimensional Representation 00:06:51