**Training Your Systems with Python Statistical Modeling**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 05m | 0.97 GB

eLearning | Skill level: All Levels

Learn statistical analysis by using various machine learning models

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.

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.

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

**+ Table of Contents**

**Classical Statistical Analysis**

1 The Course Overview

2 Computing Descriptive Statistics with Pandas

3 Confidence Intervals and Classical Hypothesis Testing вЂ“Proportions

4 Confidence Intervals and Classical Hypothesis Testing вЂ“ Mean

5 Diving into Bayesian Analysis

6 Bayesian Posterior Analysis вЂ“Proportions

7 Bayesian Posterior Analysis вЂ“Mean

8 Finding Correlations Using Pandas and SciPy

**Introduction to Supervised Learning**

9 Exploring Various Machine Learning Principles

10 Training Machine Learning Models

11 Evaluating Model Results

**Binary Prediction Models**

12 Classifying Data in Python Using the k-Nearest Neighbors (KNN)

13 Working with Decision Trees

14 Machine Learning Using Random Forests

15 Making Predictions Using the Naive Bayes Algorithm

16 Working with Support Vector Machines (SVM) for Classification and Detection

17 Logistic Regression with Machine Learning

18 Going Beyond Binary

**Regression Analysis and How to Use It**

19 Linear Models and OLS

20 Evaluating a Linear Model

21 Exploring the Bayesian Perspective of Linear Models

22 Employing Ridge Regression

23 Employing LASSO Regression

24 Spline Interpolation Using SciPy

**Thinking Machines вЂ“ Neural Networks**

25 The Perceptron

26 Neural Network Model

27 Training a Neural Network

28 Regression with Neural Networks

**Clustering**

29 Diving into Clustering and Unsupervised Learning

30 k-Means Clustering

31 Evaluating Clustering Model Results

32 Hierarchical Clustering

33 Spectral Clustering

**Dimensionality Reduction and How ItвЂ™s Done**

34 Objective of Dimensionality Reduction

35 Principal Component Analysis (PCA)

36 SVD

37 Low-Dimensional Representation