Predictive Modeling with Python

本课程是 Applied Data Analytics 专项课程的一部分

位教师：Edureka

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6个模块

深入了解一个主题并学习基础知识。

中级等级

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2 周完成

在 10 小时一周

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6个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Manage and preprocess data for statistical analysis and modeling.
Conduct hypothesis testing using advanced statistical techniques.
Build exploratory data analysis (EDA) models to uncover insights.
Build and evaluate models to solve real-world data challenges.

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作业

23 项作业

授课语言：英语（English）

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该课程共有6个模块

This Predictive Modeling with Python course provides a practical introduction to statistical analysis and machine learning with Python. You will learn essential machine learning concepts, methods, and algorithms with a strong emphasis on applying them to solve real-world business and data problems.

By the end of the course, you will: - Understand different data types used in statistical analysis. - Learn techniques to manage inconsistent data effectively. - Perform hypothesis testing using parametric and non-parametric tests. - Develop exploratory data analysis (EDA) models using statistical and machine learning methods. - Enhance machine learning models through evaluation and optimization techniques. This course is designed for individuals with a foundational knowledge of Python programming and basic statistical concepts. This course is ideal for aspiring data analysts, data scientists, business executives, machine learning engineers, and anyone passionate about data-driven decision-making Throughout the program, you will gain hands-on experience in statistical and predictive modeling and apply your skills to real-world scenarios. Enroll in "Predictive Modeling with Python" today and take your expertise to the next level!

In the first module of this course, learners will explore various data types and utilize different measures of central tendency and measures of dispersion to address data inconsistencies.

涵盖的内容

12个视频3篇阅读材料4个作业2个讨论话题

12个视频总计40分钟

Course Introduction 3分钟
Data Types 3分钟
Categorical Data 2分钟
Nominal Data 2分钟
Demonstration of Data Types - Dataset Description 4分钟
Demonstration of Types 5分钟
What is Statistics? 4分钟
Measures of Central Tendency 3分钟
Demonstration of Central Tendency 4分钟
Measures of Dispersion 3分钟
Demonstration: Measures of Dispersion 4分钟
Summary of Data and Information 2分钟

3篇阅读材料总计30分钟

Welcome to Predictive Modeling with Python 10分钟
Applications of Central Tendency in Statistics 10分钟
Libraries for Statistical Analysis 10分钟

4个作业总计29分钟

Practice Quiz : Data Types in Statistics 3分钟
Practice Quiz : Statistical Analysis 3分钟
Practice Quiz : Statistical Dispersion 3分钟
Knowledge Check : Data and Information 20分钟

2个讨论话题总计20分钟

Introduce Yourself 10分钟
Why are measures of dispersion important in understanding datasets? 10分钟

In this module, learners will learn to manage data using probability distribution functions. Learners will start by applying the Bernoulli distribution to model categorical data, explore the Poisson distribution for forecasting, and utilize the Exponential and Normal distributions for regression modeling.

涵盖的内容

17个视频3篇阅读材料5个作业

17个视频总计69分钟

Probability Density and Mass Function 4分钟
Cumulative Distribution Function 4分钟
Discrete Probability 2分钟
Negative Bernoulli Distribution 2分钟
Demonstration of Negative Bernoulli Distribution 7分钟
Geometric Distribution 2分钟
Demonstration of Geometric Distribution 7分钟
Poisson Distribution 5分钟
Example of Poisson Distribution 2分钟
Demonstration of Poisson Distribution 4分钟
Continuous Probability Distribution 5分钟
Uniform Distribution 4分钟
Exponential Distribution 3分钟
Demonstration of Exponential Distribution 3分钟
Normal Distribution 6分钟
Demonstration of Normal Distribution 6分钟
Summary of Probability Distribution Functions 2分钟

3篇阅读材料总计30分钟

Example of PDF and PMF 10分钟
Importance of Negative Bernoulli and Geometric Distributions 10分钟
Continuous Probability Distribution and Uniform Distribution: Mathematical Example 10分钟

5个作业总计32分钟

Practice Quiz : General Probability Distribution 3分钟
Practice Quiz : Negative Bernoulli and Geometric Distribution 3分钟
Practice Quiz : Poisson and Uniform Distribution 3分钟
Practice Quiz : Exponential and Normal Distribution 3分钟
Knowledge Check : Probability Distribution Function 20分钟

In the third module of this course, Learners will learn to apply the Central Limit Theorem in scenarios where data may be improperly distributed. Identify and analyze sample data, using both parametric and non-parametric methods to handle various test cases for hypothesis testing and decision-making.

涵盖的内容

30个视频3篇阅读材料5个作业1个讨论话题

30个视频总计138分钟

Central Limit Theorem 5分钟
Demonstration of Central Limit Theorem 6分钟
Demontration: Conclusion of Central Limit Theorem 3分钟
Population and Sample Space 3分钟
Parameter and Statistics 5分钟
Forms of Inferential Statistics 1分钟
Point and Interval Estimation 4分钟
Maximum Likelihood 4分钟
Demonstration Exploring Data 5分钟
Demonstration: Drawing Sample Data 4分钟
Hypothesis Testing 5分钟
Hypothesis Testing Example 5分钟
Statistical Test Implementation 6分钟
One Tailed and Two Tailed Test 5分钟
Z - Test and T - Test 6分钟
Power Analysis 6分钟
Demonstration of Confidence Interval and Margin of Error 3分钟
Demonstration of Hypothesis Testing 4分钟
Demonstrating Power Analysis 8分钟
Chi - Square Test 4分钟
Pearson and Spearman Correlation 6分钟
Chi square Test Demonstration 6分钟
Pearson Correlation Demonstration 6分钟
Spearman Correlation Demonstration 7分钟
ANOVA 6分钟
Example for One Way ANOVA - Part 1 2分钟
Example for Two Way ANOVA - Part 2 2分钟
Demonstration for One way ANOVA 5分钟
Demonstration for Two way ANOVA 3分钟
Summary for Inferential Statistics 2分钟

3篇阅读材料总计30分钟

Central Limit Theorem (CLT): Mathematical Example 10分钟
Statistical Inference Real World Applications 10分钟
Shapiro-Wilk Test 10分钟

5个作业总计32分钟

Practice Quiz : Introduction to Central Limit Theorem 3分钟
Practice Quiz : Statistical Inference Methods 3分钟
Practice Quiz : Statistical Hypothesis and Significance Testing 3分钟
Practice Quiz : Parametric and Non Parametric Tests 3分钟
Knowledge Check : Inferential Statistics 20分钟

1个讨论话题总计10分钟

Have any of the you used the above methods in their professional careers 10分钟

In the fourth module, learners will explore implementing Exploratory Data Analysis (EDA) on large, complex datasets by conducting both univariate and multivariate analysis. They will also learn how to clean and process data, as well as perform feature engineering to prepare the data for analysis.

涵盖的内容

29个视频3篇阅读材料4个作业1个讨论话题

29个视频总计138分钟

What is EDA? 5分钟
Univariate Analysis: Data and Outliers 4分钟
Univariate Analysis: Kurtosis and Chart Types 4分钟
Multivariate Analysis 4分钟
Multivariate Analysis: Covariance, Correlation, and Association 4分钟
Multivariate Analysis: Correlation Matrix 4分钟
Multivariate Analysis: Scatter Plots and HeatMaps 6分钟
Identifying and Handling Missing Data 7分钟
Sampling Methods 4分钟
Mean Median Mode Imputation 5分钟
Data Normalization and Standardization 3分钟
Methods to Transform Data 6分钟
Univariate, Bivariate and Multivariate Imputation 4分钟
Demonstration I: Understanding the Data 4分钟
Demonstration II: Visualizing and Handling Missing Data 6分钟
Demonstration III: Scaling and Imputation of Data 4分钟
Demonstration IV: Train Test Split 7分钟
Demonstration V: Stratified K-Fold Cross-Validation 4分钟
Demonstration VI: Sampling and Evaluation 4分钟
Introduction to Feature Engineering 5分钟
Feature Transformation 6分钟
Encoding: One Hot Encoding 6分钟
Encoding: Label Encoding 2分钟
Autofeat Library 3分钟
Demonstration I: Setting up the Scenario 4分钟
Demonstration II: Data Transformation 7分钟
Demonstration III: Encoding 8分钟
Demonstration IV: Autofeat 7分钟
Summary for Introduction to EDA 2分钟

3篇阅读材料总计30分钟

Understanding Exploratory Data Analysis (EDA) 10分钟
Best Practices in Data Pre-processing 10分钟
Overview of Autofeat library 10分钟

4个作业总计29分钟

Practice Quiz : Understanding EDA 3分钟
Practice Quiz : Data Cleaning and Pre-processing 3分钟
Practice Quiz : Feature Engineering and Data Transformation 3分钟
Knowledge Check : Introduction to (Exploratory Data Analysis) EDA 20分钟

1个讨论话题总计10分钟

Explain the importance of Data Pre-processing 10分钟

In this module, learners will learn how to use machine learning models to extract insights from data. They will apply regression and classification algorithms and then optimize the results produced by these models.

涵盖的内容

42个视频2篇阅读材料4个作业1个讨论话题

42个视频总计197分钟

Introduction to Linear Regression 6分钟
Assumptions in Linear Regression 4分钟
Working of Linear Regression 4分钟
Cost function in Linear Regression 2分钟
Gradient Descent in Linear Regression 3分钟
Demonstration of Linear Regression: Building Model 3分钟
Demonstration of Linear Regression: Testing the Model 6分钟
Logistic Regression 7分钟
Cost function in Logistic Regression 4分钟
Gradient Descent in Logistic Regression 4分钟
Importance of Sigmoid Function 1分钟
Demonstration: Logistic Regression - Data Processing 6分钟
Demonstration: Logistic Regression - Model Execution 7分钟
Classification in Machine Learning 5分钟
Decision Tree Part 1: What is Decision Tree? 5分钟
Decision Tree Part 2: What is Random Forest? 5分钟
Basic Terminologies of Decision Tree 4分钟
Working of Decision Tree 6分钟
Building a Decision Tree 6分钟
Advantages and Disadvantages of Decision Tree 6分钟
Demonstration Part 1: Explaining the Scenario 5分钟
Demonstration Part 2: Exploring the Data 2分钟
Demonstration Part 3: Profiling Report 7分钟
Demonstration Part 4: Attrition and Univariate Graph 7分钟
Demonstration Part 5: Data Pre - processing 4分钟
Demonstration Part 6: Building Decision Tree 4分钟
Demonstration Part 7:Tree Classifier 5分钟
Demonstration Part 8: Pros and Cons 5分钟
Random Forest Example Part 1: Ensemble Learning and Bagging 4分钟
Random Forest Example Part 2: Working of Random Forest 4分钟
Performance Metrics for Regression - MAE and MAPE 4分钟
Performance Metrics for Regression - MSE, RMSE, RMSLE and R-square 4分钟
Confusion Matrix 5分钟
ROC and AUC 5分钟
Hyperparameter Tuning and Optimization 5分钟
Model Selection 7分钟
Model Evaluation 6分钟
Bias Variance Trade-off 4分钟
Cross Validation 4分钟
Demonstration I: Grid Search - Analyze the Data 4分钟
Demonstration II: Grid Search - Building Model 4分钟
Summary of Predictive Models 2分钟

2篇阅读材料总计20分钟

Regularization in Regression 10分钟
Optuna: A Powerful Tool for Hyperparameter Optimization 10分钟

4个作业总计29分钟

Practice Quiz : Regression 3分钟
Practice Quiz : Classification: Decision Tree and Random Forest 3分钟
Practice Quiz : Model Evaluation and Optimization 3分钟
Knowledge Check : Predictive Modeling and Analysis 20分钟

1个讨论话题总计10分钟

Which among the following evaluation metrics are easy to use and fairly accurate? 10分钟

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Probability, Statistical Modeling, and Machine Learning.