Applied Machine Learning and Model Optimization

Applied Machine Learning and Model Optimization

本课程是 AI & Python Development Megaclass 专项课程的一部分

位教师：Packt - Course Instructors

访问权限由 New York State Department of Labor 提供

7个模块

深入了解一个主题并学习基础知识。

高级设置等级

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

7个模块

深入了解一个主题并学习基础知识。

高级设置等级

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

Implement various supervised and unsupervised machine learning algorithms in Python.
Apply ensemble learning techniques like Random Forests, XGBoost, and LightGBM to improve model performance.
Master model optimization techniques such as hyperparameter tuning, cross-validation, and regularization.
Evaluate machine learning models using advanced metrics and real-world validation techniques.

您将获得的技能

Applied Machine Learning
Model Evaluation
Unsupervised Learning
Performance Tuning
Dimensionality Reduction
Feature Engineering
Machine Learning Algorithms
Artificial Intelligence and Machine Learning (AI/ML)
Predictive Modeling
Supervised Learning
Data Preprocessing
Classification Algorithms
技能部分已折叠。显示 7 项技能，共 12 项。

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

9 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

了解关于 Coursera for Business 的更多信息

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 AI & Python Development Megaclass 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7个模块

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course dives deep into applied machine learning and model optimization, covering everything from foundational concepts to advanced algorithms. You'll gain hands-on experience working with different types of machine learning models, evaluating their performance, and fine-tuning them for optimal results. The course emphasizes practical, real-world applications, with interactive projects and mini-projects to ensure you can implement what you learn. Throughout the course, you'll explore core machine learning algorithms such as regression, classification, ensemble methods, and advanced techniques like XGBoost and LightGBM. You'll also focus on model optimization, including hyperparameter tuning, cross-validation, and regularization techniques. These skills will allow you to enhance the performance of your models, even in complex scenarios. This course is designed for learners who already have a basic understanding of machine learning and wish to build more advanced skills in model building and optimization. It is ideal for those looking to pursue careers in data science, machine learning engineering, or AI development. By the end of the course, you will be able to implement various machine learning algorithms, optimize model performance using hyperparameter tuning, and evaluate models effectively for real-world tasks.

In this module, we will lay the groundwork for your machine learning journey. You’ll learn essential concepts, including supervised learning and regression models, and dive into advanced techniques like polynomial regression and regularization. By the end of the module, you’ll gain hands-on experience building a supervised learning model on a real-world dataset.

涵盖的内容

8个视频2篇阅读材料1个作业

8个视频总计150分钟

Introduction to Week 5 Introduction to Machine Learning 1分钟
Day 1: Machine Learning Basics and Terminology 16分钟
Day 2: Introduction to Supervised Learning and Regression Models 16分钟
Day 3: Advanced Regression Models – Polynomial Regression and Regularization 35分钟
Day 4: Introduction to Classification and Logistic Regression 24分钟
Day 5: Model Evaluation and Cross-Validation 16分钟
Day 6: k-Nearest Neighbors (k-NN) Algorithm 17分钟
Day 7: Supervised Learning Mini Project 25分钟

2篇阅读材料总计20分钟

Introduction to the Course 'Applied Machine Learning and Model Optimization' 10分钟
Full Specialization Resources 10分钟

1个作业总计15分钟

Introduction to Machine Learning - Assessment 15分钟

In this module, we will focus on improving your machine learning models through feature engineering and model evaluation. You’ll learn how to scale, normalize, and encode data, create new features, and select the best ones. The module also covers crucial model evaluation techniques to ensure your models are robust and performant.

涵盖的内容

8个视频1个作业

8个视频总计119分钟

Introduction to Week 6 Feature Engineering and Model Evaluation 1分钟
Day 1: Introduction to Feature Engineering 15分钟
Day 2: Data Scaling and Normalization 16分钟
Day 3: Encoding Categorical Variables 17分钟
Day 4: Feature Selection Techniques 16分钟
Day 5: Creating and Transforming Features 18分钟
Day 6: Model Evaluation Techniques 16分钟
Day 7: Cross-Validation and Hyperparameter Tuning 20分钟

1个作业总计15分钟

Feature Engineering and Model Evaluation - Assessment 15分钟

In this module, we will take your machine learning models to the next level by exploring advanced algorithms. You will dive into ensemble learning methods, including bagging, boosting, and algorithms like XGBoost and CatBoost. By the end of this module, you’ll be able to handle imbalanced data and apply ensemble learning to improve model performance.

涵盖的内容

8个视频1个作业

8个视频总计126分钟

Introduction to Week 7 Advanced Machine Learning Algorithms 1分钟
Day 1: Introduction to Ensemble Learning 15分钟
Day 2: Bagging and Random Forests 14分钟
Day 3: Boosting and Gradient Boosting 16分钟
Day 4: Introduction to XGBoost 20分钟
Day 5: LightGBM and CatBoost 20分钟
Day 6: Handling Imbalanced Data 17分钟
Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset 23分钟

1个作业总计15分钟

Advanced Machine Learning Algorithms - Assessment 15分钟

In this module, we will delve into the crucial aspects of model tuning and optimization. You will learn how to fine-tune hyperparameters, apply regularization techniques, and explore advanced optimization methods like Bayesian optimization. The module also includes automation tools like GridSearchCV to speed up the hyperparameter tuning process, ensuring better model performance.

涵盖的内容

8个视频1个作业

8个视频总计126分钟

Introduction to Week 8 Model Tuning and Optimization 1分钟
Day 1: Introduction to Hyperparameter Tuning 14分钟
Day 2: Grid Search and Random Search 16分钟
Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization 27分钟
Day 4: Regularization Techniques for Model Optimization 13分钟
Day 5: Cross-Validation and Model Evaluation Techniques 13分钟
Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV 19分钟
Day 7: Optimization Project – Building and Tuning a Final Model 23分钟

1个作业总计15分钟

Model Tuning and Optimization - Assessment 15分钟

In this section, we will guide you through a variety of intermediate-level projects that will enhance your programming abilities. You’ll work on real-world tools like weather dashboards, expense trackers, and interactive games. This hands-on approach will help you solidify your skills while creating practical applications for daily use.

涵盖的内容

11个视频1个作业

11个视频总计206分钟

Day 50: Weather Dashboard App 29分钟
Day 51: Expense Tracker 18分钟
Day 52: File Organizer Tool 17分钟
Day 53: Tic-Tac-Toe Game 25分钟
Day 54: Mini Chatbot 11分钟
Day 55: Music Playlist Organizer 20分钟
Day 56: Personal Budget Planner 14分钟
Day 57: ASCII Art Generator 15分钟
Day 58: Pomodoro Timer 22分钟
Day 59: Markdown to HTML Converter 17分钟
Day 60: Personal Diary App 18分钟

1个作业总计15分钟

Intermediate Projects - Assessment 15分钟

In this module, we will focus on advanced intermediate projects that challenge your skills further. You’ll work on building dynamic applications such as a movie recommendation system, stock market dashboard, and portfolio website backend. These projects will also deepen your understanding of web scraping, task automation, and data visualization.

涵盖的内容

10个视频1个作业

10个视频总计143分钟

Day 61: Social Media Scraper 13分钟
Day 62: Automated Backup Tool 13分钟
Day 63: Movie Recommendation System 16分钟
Day 64: PDF Merger Tool 11分钟
Day 65: Portfolio Website Backend 21分钟
Day 66: Flashcards Learning App 10分钟
Day 67: Stock Market Dashboard 16分钟
Day 68: Task Scheduler 15分钟
Day 69: Currency Converter 11分钟
Day 70: Data Visualizer App 18分钟

1个作业总计15分钟

Advanced Intermediate Projects - Assessment 15分钟

In this module, you will explore and implement a wide variety of machine learning algorithms in Python. From supervised learning techniques like linear regression and SVM to unsupervised algorithms like K-Means and DBSCAN, you will gain hands-on experience with each method. The module also covers advanced deep learning algorithms such as CNNs, RNNs, and Transformers for tackling complex tasks like image classification and natural language processing.

涵盖的内容

28个视频1篇阅读材料3个作业

28个视频总计189分钟

Introduction to Machine Learning Algorithms and Implementation in Python 4分钟
1. Supervised Learning Algorithms: Linear Regression Implementation 6分钟
2. Supervised Learning Algorithms: Ridge and Lasso Regression Implementation 8分钟
3. Supervised Learning Algorithms: Polynomial Regression Implementation 7分钟
4. Supervised Learning Algorithms: Logistic Regression Implementation 6分钟
5. Supervised Learning Algorithms: K-Nearest Neighbors (KNN) Implementation 6分钟
6. Supervised Learning Algorithms: Support Vector Machines (SVM) Implementation 6分钟
7. Supervised Learning Algorithms: Decision Trees Implementation 6分钟
8. Supervised Learning Algorithms: Random Forests Implementation 6分钟
9. Supervised Learning Algorithms: Gradient Boosting Implementation 6分钟
10. Supervised Learning Algorithms: Naive Bayes Implementation 6分钟
11. Unsupervised Learning Algorithms: K-Means Clustering Implementation 4分钟
12. Unsupervised Learning Algorithms: Hierarchical Clustering Implementation 5分钟
13. Unsupervised Learning Algorithms: DBSCAN 5分钟
14. Unsupervised Learning Algorithms: Gaussian Mixture Models (GMM) 5分钟
15. Unsupervised Learning Algorithms: Principal Component Analysis (PCA) 5分钟
16. Unsupervised Learning Algorithms: t-Distributed Stochastic Neighbor Embedding 5分钟
17. Unsupervised Learning Algorithms: Autoencoders Implementation 8分钟
18. Self-Training Implementation 7分钟
19. Q-Learning Implementation 9分钟
20. Deep Q-Networks (DQN) Implementation 14分钟
21. Policy Gradient Methods Implementation 10分钟
22. One-Class SVM Implementation 5分钟
23. Isolation Forest Implementation 5分钟
24. Convolutional Neural Networks (CNNs) Implementation 8分钟
25. Recurrent Neural Networks (RNNs) Implementation 8分钟
26. Long Short-Term Memory (LSTM) Implementation 7分钟
27. Transformers Implementation 11分钟