What prior knowledge do I need for this course?

You should be comfortable with Python basics and have a foundation in statistics or linear algebra. Prior exposure to data analysis libraries is helpful but not required.

What machine learning algorithms are covered?

The course covers regression (linear, logistic), classification, decision trees, ensemble methods, and introduces advanced algorithms like SVMs.

Can this course help me in a data science career?

Yes, it equips you with practical ML skills that are highly valued in data science, analytics, and AI career paths.

What career opportunities can machine learning skills open up?

Machine learning opens opportunities in data science, AI engineering, business analytics, and research roles across industries.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate to validate your skills and share with employers and on LinkedIn.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Applied Machine Learning with Python

本课程是 Mastering AI: Neural Nets, Vision System, Speech Recognition 专项课程的一部分

位教师：Edureka

包含在 Coursera Plus 中

了解更多

4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

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4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

1 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

Explore machine learning algorithms, including supervised, unsupervised, and semi-supervised methods.
Apply decision trees, random forests, and K-means clustering for classification and clustering.
Develop machine learning models to gain insights and make predictions from real-world data.
Enhance model accuracy by applying model-boosting techniques and evaluating their effectiveness.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

14 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Mastering AI: Neural Nets, Vision System, Speech Recognition 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

This course offers an in-depth, practical introduction to machine learning using Python, covering core concepts across supervised, unsupervised, and semi-supervised methods.

Through hands-on exercises, you will master key algorithms such as decision trees and random forests for classification, regression models for prediction, and K-means clustering to uncover patterns in unlabeled data. You will also learn how to implement model boosting techniques to enhance accuracy and apply strategies for effectively leveraging unlabeled data to improve performance. This course is designed for learners with a foundation in Python and basic statistics, making it ideal for aspiring data scientists, machine learning practitioners, and Python developers looking to deepen their skills. By the end of this course, You will be able to: - Explain and implement decision trees and random forests as classification algorithms. - Define and differentiate various types of machine learning algorithms. - Analyze the working of regression for predictive tasks. - Apply K-means clustering to explore and discover patterns in unlabeled data. - Use unlabeled data to improve model training. - Manipulate boosting algorithms to achieve higher model accuracy. Equip yourself with practical tools and advanced techniques to bring predictive power to your projects. Enroll now and advance your AI journey!

In this module, learners will explore various types of machine learning and algorithms, such as Regression, along with different evaluation metrics that evaluate machine learning models at different stages of development.

涵盖的内容

30个视频6篇阅读材料5个作业2个讨论话题1个插件

30个视频总计135分钟

Course Introduction4分钟
Machine Learning in Industry4分钟
How Companies use Machine Learning5分钟
Machine Learning Process5分钟
Steps in Machine Learning4分钟
Types of Machine Learning6分钟
Introduction to Linear Regression4分钟
Real Life Examples3分钟
Calculating OLS 6分钟
Equation of OLS3分钟
Assumptions in Linear Regression3分钟
Demonstration: Setting Up the Model 5分钟
Calculating R - Square and RMSE 4分钟
Residual Plot and Q-Q Plot 2分钟
Cook's Distance4分钟
Real - Life Examples of Logistic Regression3分钟
What is Logistic Regression5分钟
Cost Function2分钟
Assumptions in Logistic Regression3分钟
Demonstration of Logistic Regression: Transforming Data5分钟
Demonstration of Logistic Regression: Developing the Model3分钟
Confusion Matrix3分钟
Example for Calculating Confusion Matrix6分钟
Conditions for Over-Fitting and Under-Fitting4分钟
Overfitting and Underfitting5分钟
Performance Metrics - MSE, RMSE, MAE, MAPE 4分钟
R - Square, RMSLE and Adjusted R - Square4分钟
Working of R - Square4分钟
Significance of R - Square6分钟
Summary for Inception of Machine Learning2分钟

6篇阅读材料总计70分钟

Welcome to Applied Machine Learning with Python10分钟
How Companies are Crafting the Future20分钟
Machine Learning 10110分钟
Regression and its Assumptions10分钟
Role of Regularization10分钟
Evaluation of All Things Predictive10分钟

5个作业总计42分钟

Practice Quiz : AI and Augment of Machine Learning3分钟
Practice Quiz : Overview of Machine Learning3分钟
Practice Quiz : Regression3分钟
Practice Quiz : Evaluation Metrics3分钟
Knowledge Check : Introduction to Machine Learning30分钟

2个讨论话题总计20分钟

Introduce Yourself10分钟
Which of the following evaluation metrics is most suitable for Regression models?10分钟

1个插件总计10分钟

Your Experience with Machine Learning in Python10分钟

This module will cover various supervised machine learning algorithms used to model data and provide desired results and conclusions, which will help individuals or organizations make informed decisions backed by data analysis.

涵盖的内容

34个视频3篇阅读材料4个作业1个讨论话题

34个视频总计155分钟

Classification in Machine Learning5分钟
What is Decision Tree?5分钟
Decision Tree - Entropy and Information Gain5分钟
Step by Step Building of Decision Tree5分钟
Pruning in Decision Tree3分钟
Demonstration: Importing Data5分钟
Demonstration: Building Decision Tree and Random Forest 5分钟
Demonstration: Importance of Features2分钟
Demonstration: Production Ready Random Forest2分钟
Demonstration: Hyperparameter Tuning3分钟
What is SVM?4分钟
Terminologies in SVM5分钟
Hinge Loss Function and Other Parameters6分钟
Demonstration of SVM - Exploring the Data3分钟
Demonstration of SVM - Setting up the SVM Classifier6分钟
What is Naive Bayes?3分钟
Working of Naive Bayes: Bayes Theorem3分钟
Example of Naive Bayes Algorithm5分钟
Demonstration of Naive Bayes Code4分钟
Working of KNN 3分钟
Example of KNN Algorithm4分钟
Demonstration of KNN - Setting Up the Model5分钟
Demonstration of KNN - Transforming and Scaling Data4分钟
Demonstration of KNN - Creating Classifier2分钟
Dimensionality Reduction6分钟
Introduction to PCA4分钟
Applying PCA5分钟
Eigen Values and Eigen Vectors5分钟
Demonstration: Initializing PCA2分钟
Demonstration: Determining Optimal Number of Components through PCA4分钟
Demonstration: Implementing Optimal PCA5分钟
Working of LDA5分钟
Demonstration of LDA5分钟
Summary for Machine Learning Algorithms2分钟

3篇阅读材料总计60分钟

Decision Trees and Random Forests20分钟
SVM, KNN and Naive Bayes: When to Use Which Algorithm?20分钟
Best Practices for Dimensionality Reduction: PCA vs. LDA20分钟

4个作业总计48分钟

Practice Quiz : Decision Tree and Random Forest6分钟
Practice Quiz : SVM, KNN and Naive Bayes Algorithm6分钟
Practice Quiz : Dimensionality Reduction6分钟
Knowledge Check : Machine Learning Algorithms30分钟

1个讨论话题总计20分钟

Which algorithm among SVM, KNN, and Naive Bayes do you find easier to use?20分钟

This module covers association rule mining to uncover meaningful associations. Additionally, learners will explore how to build recommendation engines, which play a key role in personalizing user experiences, boosting user engagement, and driving sales across various industries.

涵盖的内容

20个视频3篇阅读材料4个作业

20个视频总计111分钟

What are Association Rules?7分钟
Apriori Algorithm5分钟
Demonstrating Apriori Algorithm7分钟
What are Recommendation Engine?6分钟
CBF6分钟
Demonstration of Recommendation Engine: Preparing Data6分钟
Demonstration: Testing the Model4分钟
Elements for Reinforcement Learning6分钟
Demonstration of Boosting: Explaining the Dataset5分钟
Demonstration of Boosting: Cleaning and Transforming Dataset5分钟
Demonstration of Boosting: Factors Affecting Promotion4分钟
Demonstration of Boosting: Total Score and Service Affecting Promotion5分钟
Demonstration of Boosting: Age , Previous Year rating Influencing Promotion3分钟
Demonstration of Boosting: Department Influencing Promotion5分钟
Demonstration of Boosting: Education Affecting Promotion and Summarization5分钟
Demonstration of Boosting: Modeling the Data4分钟
Demonstration of Boosting: Building a Model6分钟
Working of K-Means Algorithm6分钟
Demonstration of K-Means Clustering5分钟
Summary for Association Rule Mining and Recommendation System3分钟

3篇阅读材料总计55分钟

FP-Growth in Association Rule10分钟
How Recommendation Engines Personalize Your World20分钟
Training Models to Get Better with Experience25分钟

4个作业总计48分钟

Practice Quiz : Association Rules6分钟
Practice Quiz : Recommendation Engines6分钟
Practice Quiz : Reinforcement Learning and Boosting6分钟
Knowledge Check : Association Rules and Recommendation System30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 Python programming concepts, Regression Modeling, Supervised machine learning algorithms and Association rule mining.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题1个插件

1个视频总计3分钟

Course Summary for Applied Machine Learning with Python3分钟

1篇阅读材料总计30分钟

Final Project: Cab Booking Demand Analysis30分钟

1个作业总计30分钟

Knowledge Check : Applied Machine Learning30分钟

1个讨论话题总计10分钟

Describe Your Learning Journey10分钟

1个插件总计10分钟

Final Reflections on Applied Machine Learning10分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

98 门课程102,390 名学生

提供方

Edureka

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常见问题

This course, Applied Machine Learning with Python, focuses on teaching practical machine learning techniques using Python. It covers various algorithms, including decision trees, random forests, regression, and clustering, and guides learners in applying these methods to solve real-world problems.

The course emphasizes hands-on experience in building models, analyzing data, and improving model performance through techniques like boosting. By the end, learners will have the skills to implement machine learning algorithms, evaluate their effectiveness, and uncover valuable insights from data.

The Applied Machine Learning with Python course is ideal for aspiring data scientists, software developers, and professionals looking to enhance their skills in machine learning. It provides hands-on experience in building and deploying machine learning models using Python, making it perfect for those seeking to apply data-driven solutions in real-world scenarios.

The duration of this course is approximately 4 weeks, depending on the learner's pace, with an estimated commitment of 2-3 hours per week for lectures, hands-on projects, and assessments.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.