EXA AI Roadmap (Based on Berkeley's Professional Certificate in Machine Learning & AI)
EXA AI 路線圖（基於伯克利的機器學習與人工智慧專業證書）

Master the Machine Learning and Artificial Intelligence concepts and skills from the Berkeley’s Professional Certificate program, entirely for FREE! Our curated guide and resources offer a structured path to Al excellence, tailored to your individual pace.
完全免費掌握伯克利專業證書課程中的機器學習和人工智慧概念與技能！我們精心策劃的指南和資源提供了一條結構化的通往人工智慧卓越的道路，根據您的個人進度量身定制。

Hi, l'm Jean  嗨，我是珍

Outline  大綱

Background Essentials - What You Need to Know
背景要素 - 您需要知道的事項

Commitment  承諾

Future Job Titles  未來職位名稱

Recommended Tools and Resources for the Program
建議的工具和資源用於該計劃

1. Python: The primary programming language used for data analysis, machine learning, and AI development.
   Python：用於數據分析、機器學習和人工智慧開發的主要程式語言。
2. Jupyter: An interactive environment for writing and running Python code, useful for data analysis and experimentation.
   Jupyter：一個用於編寫和運行 Python 代碼的互動環境，對數據分析和實驗非常有用。
3. Pandas: A powerful library for data manipulation and analysis, particularly for structured data.
   Pandas：一個強大的數據操作和分析庫，特別適用於結構化數據。
4. Google Colab: A cloud-based platform that provides a Jupyter-like environment with free access to GPUs for accelerated computing.
   Google Colab：一個基於雲端的平台，提供類似 Jupyter 的環境，並免費訪問 GPU 以加速計算。
5. Seaborn: A Python visualization library based on Matplotlib, used for creating informative and attractive statistical graphics.
   Seaborn：一個基於 Matplotlib 的 Python 可視化庫，用於創建信息豐富且吸引人的統計圖形。
6. GitHub: A platform for version control and collaborative development, essential for sharing and tracking your projects.
   GitHub：一個版本控制和協作開發的平台，對於分享和跟踪您的項目至關重要。
7. Codio: A cloud-based environment for coding exercises and hands-on projects, offering an integrated platform for learning and practicing coding.
   Codio：一個基於雲端的編程練習和實踐項目的環境，提供一個集成的平台來學習和練習編程。
8. Plotly: A library for creating interactive, web-ready plots and visualizations, ideal for presenting data insights.
   Plotly：一個用於創建互動式、網頁準備的圖表和可視化的庫，理想用於呈現數據洞察。

SECTION 1: Berkeley's Prerequisite
第一部分：伯克利的先決條件

• An educational background in STEM fields
  在 STEM 領域的教育背景
• Technical work experience
  技術工作經驗
• Some experience with Python, R, or SQL
  一些使用 Python、R 或 SQL 的經驗
• Recommended Courses:  推薦課程：
• Python Full Roadmap by Jean
  Python 完整路線圖 by Jean
• Introduction to R by DataCamp (paid)
  DataCamp 的 R 課程介紹（付費）
• Intro to SQL by DataCamp (paid)
  DataCamp 的 SQL 入門課程（付費）
• Some experience with statistics and calculus
  一些統計學和微積分的經驗
• Recommended Courses:  推薦課程：
• Statistics and Probability by Khan Academy
  Khan Academy 的統計學與機率
• Calculus 1 by Khan Academy
  Khan Academy 的微積分 1
• Optional Resource: Full Data Science Roadmap by Jean
  可選資源：全數據科學路線圖由 Jean 提供

SECTION 2: Career Preparation and Guidance
第二部分：職業準備與指導

• The Ultimate Resume Handbook by Jean (paid): A comprehensive guide to crafting standout resumes tailored for tech roles. Also, download the free Ultimate Resume Template.
  《終極履歷手冊》由 Jean（付費）：一本全面的指南，幫助您為科技職位打造出色的履歷。此外，還可以下載免費的終極履歷模板。
• Developer Resume with ChatGPT for ATS Success by Jean on YouTube: Learn how to use ChatGPT effectively to optimize your resume for applicant tracking systems.
  開發者履歷與 ChatGPT 以達成 ATS 成功，由 Jean 在 YouTube 提供：學習如何有效使用 ChatGPT 來優化您的履歷以符合申請者追蹤系統。
• Engineering Resume Hack (from Big Tech Hiring Manager) by Jean on YouTube: Insider tips from Jean, a former hiring manager, to make your resume stand out in big tech.
  工程履歷黑客（來自大型科技公司招聘經理）由 Jean 在 YouTube 上提供：前招聘經理 Jean 的內部提示，幫助你的履歷在大型科技公司中脫穎而出。

Interviews:  面試：

• Cracking the Coding Interview by Gayle L. McDowell (paid book)
  Cracking the Coding Interview by Gayle L. McDowell (付費書籍)
• Blind 75 Leet code questions by Leetcode
  Blind 75 Leet code 問題 by Leetcode
• Python cheat sheet by Leetcode
  Leetcode 的 Python 速查表
• DSA study quide by Leetcode
  DSA 學習指南 by Leetcode
• System Design Interview Survival Guide (2024): Strategies and Tips (blog)
  系統設計面試生存指南（2024）：策略和技巧（部落格）

• What it is: An open session where Jean answers your career-related questions live-completely free.
  它是：一個開放的會議，讓讓·回答您與職業相關的問題，完全免費。
• Why it matters: Gain expert insights on navigating the tech industry, job search strategies, and career development.
  為什麼這很重要：獲得專家對於如何在科技行業中導航、求職策略和職業發展的見解。
• Next session: January 6, 2025. Sign up here. Follow Jean on Linkedln to get updates on the newest events.
  下一場次：2025 年 1 月 6 日。點此報名。關注 Jean 的 LinkedIn 以獲取最新活動的更新。

• Tech Salaries Trends for 2025 by Jean on YouTube: Stay informed about the latest salary trends in the tech industry.
  2025 年科技薪資趨勢，作者：Jean，於 YouTube 上發佈：隨時了解科技產業最新的薪資趨勢。
• Top Machine Learning Engineer Salary by Jean on YouTube: Explore the earning potential of ML roles in various industries.
  頂尖機器學習工程師薪資由 Jean 在 YouTube 上提供：探索各行各業中機器學習職位的賺錢潛力。

Career Development:  職業發展：

• What Color Is Your Parachute? By Richard N. Bolles (Paid book)
  《你的降落傘是什麼顏色？》 作者：理查德·N·博爾斯（付費書籍）
• Zero to AI ML Engineer: Get Hired Without Experience by Jean on YouTube: A roadmap for breaking into $\mathrm{Al}/\mathrm{ML}$$\mathrm{Al}/\mathrm{ML}$Al//ML\mathrm{Al} / \mathrm{ML} engineering without prior experience.
  從零開始成為 AI 機器學習工程師：在 YouTube 上由 Jean 提供的無經驗求職指南：一個進入 $\mathrm{Al}/\mathrm{ML}$$\mathrm{Al}/\mathrm{ML}$Al//ML\mathrm{Al} / \mathrm{ML} 工程領域的路線圖。
• 7 Habits That Will Make You a Better Programmer by Jean on YouTube: Simple habits to improve your coding skills and professional growth.
  7 個讓你成為更好程式設計師的習慣，作者：Jean，於 YouTube 上發布：簡單的習慣來提升你的編碼技能和職業成長。
• 7 Mistakes that Ruin Your Career as a Junior Software Engineer by Jean on YouTube: Avoid common pitfalls that could derail your early career.
  7 個毀掉你作為初級軟體工程師職業生涯的錯誤，作者：Jean，於 YouTube 上發佈：避免可能會破壞你早期職業生涯的常見陷阱。

For Community:  為社區：

• Join the free Linkedln private group, Achieve Together with EXA: Connect with like-minded professionals for accountability, support, and collaboration while following this roadmap.
  加入免費的 LinkedIn 私人群組，與 EXA 一起實現：與志同道合的專業人士聯繫，以便在遵循這個路線圖的同時獲得責任感、支持和合作。

SECTION 3: Foundations of ML/AI
第三部分：機器學習/人工智慧的基礎

• Description: Introduces fundamental machine learning concepts such as supervised and unsupervised learning.
  描述：介紹基本的機器學習概念，例如監督式學習和非監督式學習。
• Course Recommendation:  課程推薦：
• CS 198-126: Lecture 1 - Intro to Machine Learning on YouTube: Visit https://ml.berkeley.edu/decal/modern-cv to see more information about the course, including slides, assignments, lectures, and course information/background.
  CS 198-126: Lecture 1 - Intro to Machine Learning on YouTube: 訪問 https://ml.berkeley.edu/decal/modern-cv 以查看有關課程的更多信息，包括幻燈片、作業、講座和課程信息/背景。

Module 2: Fundamentals of Statistics and Distribution Functions
模組 2：統計學基礎與分佈函數

• Description:  描述：
• Learn statistical techniques such as measures of central tendency, variance, and different probability distributions.
  學習統計技術，例如集中趨勢的測量、變異數和不同的概率分佈。
• Understand the importance of statistics for model evaluation and data-driven decision-making.
  了解統計在模型評估和數據驅動決策中的重要性。
• Course Recommendation:  課程推薦：
• Khan Academy’s Statistics and Probability: A comprehensive course that covers statistical concepts like distributions, variance, and probability, making it perfect for beginners.
  可汗學院的統計與概率：一門全面的課程，涵蓋了分佈、方差和概率等統計概念，非常適合初學者。
• A First Course in Probability, by Sheldon Ross, Pearson (paid resource)
  《概率論入門》，謝爾登·羅斯著，培生（付費資源）

Module 3: Introduction to Data Analytics
模組 3：數據分析導論

• Description:  描述：
• Discover the basics of data analytics, including data collection, cleaning, and preparation.
  了解數據分析的基本知識，包括數據收集、清理和準備。
• Gain an overview of how analytics informs decision-making in businesses.
  獲得分析如何影響企業決策的概覽。
• Course Recommendation:  課程推薦：
• Exploratory Data Analysis in Python by DataCamp: Provides an introduction to data analytics concepts and tools for data cleaning, preparation, and visualization.
  DataCamp 的 Python 探索性數據分析：提供數據分析概念和工具的介紹，用於數據清理、準備和可視化。
• Python for Data Analysis by Wes McKinney (Paid book)
  Python 數據分析，作者：Wes McKinney（付費書籍）

Module 4: Fundamentals of Data Analytics
模組 4：數據分析基礎

• Description:  描述：
• Build on the previous module with core data analytics concepts such as data wrangling, feature selection, and exploratory data analysis.
  基於前一模組，涵蓋核心數據分析概念，如數據整理、特徵選擇和探索性數據分析。
• Focus on hands-on practice using Python libraries like Matplotlib and Pandas to manipulate and visualize data effectively.
  專注於使用 Python 函式庫，如 Matplotlib 和 Pandas，進行實作練習，以有效地操作和視覺化數據。
• Course Recommendation:  課程推薦：
• Introduction to Data Visualization with Matplotlib by DataCamp
  DataCamp 的 Matplotlib 數據視覺化入門
• Joining Data with Pandas by DataCamp

Module 5: Practical Applications I
模組五：實用應用 I

1. Beginner: Exploratory Data Analysis (EDA) on a Public Dataset
   初學者：對公共數據集進行探索性數據分析（EDA）

• Goal: Perform an in-depth exploratory data analysis (EDA) on a publicly available dataset to understand the underlying patterns, relationships, and insights.
  目標：對一個公開可用的數據集進行深入的探索性數據分析（EDA），以了解潛在的模式、關係和見解。
• Data Source: Choose a publicly available dataset like the Iris Dataset, Titanic Dataset, or COVID-19 dataset (available on Kaggle or UCI Machine Learning Repository).
  數據來源：選擇一個公開可用的數據集，如虹膜數據集、泰坦尼克號數據集或 COVID-19 數據集（可在 Kaggle 或 UCI 機器學習庫上獲得）。
• Skills Learned:  學到的技能：
• Data cleaning and preprocessing (handling missing values, outliers, and duplicates).
  數據清理和預處理（處理缺失值、異常值和重複值）。
• Data visualization (using libraries like Matplotlib, Seaborn, and Plotly).
  數據視覺化（使用像 Matplotlib、Seaborn 和 Plotly 的庫）。
• Descriptive statistics (mean, median, mode, standard deviation).
  描述性統計（平均數、中位數、眾數、標準差）。
• Correlation analysis and basic feature engineering.
  相關分析和基本特徵工程。
• Summarizing findings in an interactive Jupyter notebook or a dashboard.
  在互動式 Jupyter 筆記本或儀表板中總結發現。
• Outcome: A report or presentation showcasing key findings, trends, and visualizations from the dataset, highlighting significant insights.
  結果：一份報告或演示文稿，展示數據集中的關鍵發現、趨勢和可視化，突顯重要見解。

2. Intermediate: Predicting House Prices Using Regression Models
   中級：使用回歸模型預測房價

• Goal: Develop a model to predict house prices based on various features like square footage, number of bedrooms, location, etc.
  目標：開發一個模型，以根據平方英尺、臥室數量、位置等各種特徵預測房價。
• Data Source: Use the Ames Housing Dataset or any other similar housing dataset.
  數據來源：使用艾姆斯住房數據集或任何其他類似的住房數據集。
• Skills Learned:  學到的技能：
• Data cleaning (handling missing values, encoding categorical variables, scaling).
  數據清理（處理缺失值、編碼類別變量、縮放）。
• Feature selection and engineering (selecting important variables, creating new features).
  特徵選擇和工程（選擇重要變數，創建新特徵）。
• Regression models (Linear Regression, Multiple Linear Regression).
  回歸模型（線性回歸、多元線性回歸）。
• Model evaluation techniques ( ${\mathrm{R}}^2$${\mathrm{R}}^2$R^(2)\mathrm{R}^{2}, Mean Absolute Error, etc.).
  模型評估技術（ ${\mathrm{R}}^2$${\mathrm{R}}^2$R^(2)\mathrm{R}^{2} ，平均絕對誤差等）。
• Visualizing model performance and understanding feature importance.
  可視化模型性能和理解特徵重要性。
• Outcome: A predictive model with performance metrics to evaluate its accuracy in predicting house prices, along with a comprehensive report detailing the steps involved and analysis of the results.
  結果：一個具有性能指標的預測模型，用於評估其在預測房價方面的準確性，以及一份詳細說明所涉及步驟和結果分析的綜合報告。

3. Intermediate: Customer Segmentation Using Clustering Algorithms
   中級：使用聚類算法進行客戶細分

• Goal: Segment customers into distinct groups based on their purchasing behavior and demographics, helping businesses tailor marketing strategies.
  目標：根據客戶的購買行為和人口統計特徵將其劃分為不同的群體，幫助企業量身定制營銷策略。
• Data Source: Use a dataset like the Mall Customer Segmentation Dataset or any dataset with demographic and purchasing data (available on Kaggle).
  數據來源：使用像商場顧客細分數據集或任何具有人口統計和購買數據的數據集（可在 Kaggle 上獲得）。

- Skills Learned:  - 學到的技能：

• Data preprocessing (normalization, dealing with categorical variables).
  數據預處理（正規化，處理類別變量）。
• Clustering algorithms (K-means, DBSCAN, Hierarchical Clustering).
  聚類演算法（K-means、DBSCAN、層次聚類）。
• Dimensionality reduction (PCA for visualizing high-dimensional data).
  降維（主成分分析用於可視化高維數據）。
• Evaluating clustering results (Silhouette Score, Elbow Method).
  評估聚類結果（輪廓分數，肘部法則）。
• Visualizing clusters using tools like Seaborn, Plotly, and PCA.
  使用 Seaborn、Plotly 和 PCA 等工具可視化聚類。
• Outcome: A customer segmentation model with visualizations of the clusters and a business-driven interpretation of each segment, identifying the key characteristics of each group.
  結果：一個客戶細分模型，包含集群的可視化以及對每個細分的商業驅動解釋，識別每個群體的關鍵特徵。

SECTION 4: ML/AI Techniques
第 4 節：機器學習/人工智慧技術

Module 6: Clustering and Principal Component Analysis (PCA)
模組 6：聚類與主成分分析 (PCA)

• Clustering is a method that groups similar data points together so that things in the same group are more alike than things in different groups.
  聚類是一種將相似數據點分組的方法，使得同一組中的事物比不同組中的事物更相似。

- Clustering on Scikit-Learn.
- 在 Scikit-Learn 上的聚類。

• Principal Component Analysis (PCA) is a technique that reduces the number of variables in a dataset by combining them into new, simpler variables while keeping most of the important information.
  主成分分析（PCA）是一種通過將數據集中的變量合併為新的、更簡單的變量來減少變量數量的技術，同時保留大部分重要信息。
• PCA on Sci-kit-learn.  PCA 在 Sci-kit-learn 上。

Module 7: Linear and Multiple Regressions
模組 7：線性和多重回歸

• Linear Regression is a method used to predict a value by finding a relationship between that value and one or more other variables.
  線性回歸是一種通過尋找該值與一個或多個其他變量之間的關係來預測值的方法。
• StatQuest’s Linear Regression on YouTube: An accessible breakdown of linear regression concepts.
  StatQuest 的線性回歸在 YouTube 上：對線性回歸概念的易懂解析。
• Multiple Regression is a technique used to predict a value by considering multiple factors or variables that might influence it.
  多重回歸是一種通過考慮可能影響某個值的多個因素或變量來預測該值的技術。
• Khan Academy’s Multiple Regression: A comprehensive resource explaining multiple regression and its applications.
  可汗學院的多重回歸：一個全面的資源，解釋多重回歸及其應用。

Module 8: Feature Engineering and Overfitting
模組 8：特徵工程與過擬合

• Feature Engineering is the process of creating new input features or modifying existing ones to improve the performance of a machine-learning model.
  特徵工程是創建新輸入特徵或修改現有特徵的過程，以提高機器學習模型的性能。
• Kaggle Micro-Course: Feature Engineering
  Kaggle 微課程：特徵工程
• Overfitting happens when a model learns the details of the training data too well, including noise or errors, making it perform poorly on new, unseen data.
  過擬合發生在模型過於深入地學習訓練數據的細節，包括噪聲或錯誤，導致其在新的、未見過的數據上表現不佳。
• What is Overfitting (blog)
  什麼是過擬合（部落格）

Module 9: Model Selection and Regularization
模組 9：模型選擇與正則化

• Model Selection:  模型選擇：
• Learn techniques to evaluate and choose the best-performing model for a given dataset, including cross-validation.
  學習評估和選擇最佳表現模型的技術，適用於給定的數據集，包括交叉驗證。
• Model selection and evaluation on Scikit Learn: Covers model evaluation and selection.
  在 Scikit Learn 上的模型選擇和評估：涵蓋模型評估和選擇。
• Regularization is a technique used to prevent overfitting by adding a penalty to the model, making it simpler and less likely to fit noise in the data.
  正則化是一種通過向模型添加懲罰來防止過擬合的技術，使模型更簡單並且不太可能擬合數據中的噪聲。
• Google’s Crashcourse on Overfitting: L2 regularization: Provides insights into real-world applications of regularized models.
  Google 的過擬合速成課程：L2 正則化：提供有關正則化模型在現實世界應用的見解。

Module 10: Time Series Analysis and Forecasting
模組 10：時間序列分析與預測

• Time Series Analysis is the process of analyzing data points collected or recorded at specific time intervals to identify trends, patterns, and make predictions about the future.
  時間序列分析是分析在特定時間間隔內收集或記錄的數據點的過程，以識別趨勢、模式並對未來進行預測。
• Forecasting is the process of predicting future events or trends based on past data.
  預測是根據過去數據預測未來事件或趨勢的過程。
• Resource: Kaggle Micro-Course on Time Series and Forecasting: Focuses on time series data manipulation and visualization.
  資源：Kaggle 微課程：時間序列與預測：專注於時間序列數據的操作和可視化。

Module 11: Practical Application II
模組 11：實用應用 II

1. Customer Segmentation and Behavior Analysis
1. 客戶細分與行為分析

• Goal: Segment customers into distinct groups based on purchasing behavior and demographic features, and forecast their future spending.
  目標：根據購買行為和人口特徵將客戶劃分為不同的群體，並預測他們未來的消費。
• Techniques Used:  使用的技術：
• Clustering & PCA: Use clustering (e.g., K-means) to group customers with similar behaviors. Apply PCA for dimensionality reduction to visualize customer data in fewer dimensions while retaining key features.
  聚類與主成分分析：使用聚類（例如，K-means）將具有相似行為的客戶分組。應用主成分分析進行降維，以在保留關鍵特徵的同時，將客戶數據可視化為較少的維度。
• Feature Engineering & Overfitting: Engineer features such as customer tenure, average purchase value, and frequency of purchases. Use regularization (Lasso or Ridge) to avoid overfitting in your clustering models and regression analysis.
  特徵工程與過擬合：工程特徵，例如客戶任期、平均購買價值和購買頻率。使用正則化（Lasso 或 Ridge）來避免在您的聚類模型和回歸分析中出現過擬合。
• Linear and Multiple Regressions: Implement linear regression to predict future spending based on demographic and behavioral features. Use multiple regression to predict more complex relationships between customer features and spending habits.
  線性和多重回歸：實施線性回歸以根據人口統計和行為特徵預測未來支出。使用多重回歸來預測客戶特徵與消費習慣之間的更複雜關係。

2. Sales Forecasting for Retail Business
2. 零售業銷售預測

• Goal: Predict future sales for various products in different regions, accounting for seasonality and external factors.
  目標：預測不同地區各種產品的未來銷售，考慮季節性和外部因素。
• Techniques Used:  使用的技術：
• Time Series Analysis & Forecasting: Use time series techniques (e.g., ARIMA or Prophet) to model and forecast sales data, incorporating seasonality and holidays.
  時間序列分析與預測：使用時間序列技術（例如，ARIMA 或 Prophet）來建模和預測銷售數據，納入季節性和假期。
• Clustering & PCA: Apply clustering algorithms to group stores or products with similar sales patterns. Use PCA to reduce dimensionality in product features while maintaining key sales drivers.
  聚類與主成分分析：應用聚類算法將銷售模式相似的商店或產品分組。使用主成分分析來降低產品特徵的維度，同時保持關鍵銷售驅動因素。
• Feature Engineering & Overfitting: Engineer features such as promotional periods, holidays, and store location. Regularize your models using Lasso or Ridge to avoid overfitting to noisy historical data.
  特徵工程與過擬合：工程特徵，例如促銷期間、假期和商店位置。使用 Lasso 或 Ridge 對模型進行正則化，以避免對嘈雜的歷史數據過擬合。
• Linear and Multiple Regressions: Use linear regression to forecast sales for each store, considering factors like pricing, promotions, and weather conditions. Use multiple regression models to understand the combined effect of various factors on sales.
  線性和多重回歸：使用線性回歸預測每個商店的銷售，考慮價格、促銷和天氣條件等因素。使用多重回歸模型來理解各種因素對銷售的綜合影響。

Module 12: Classification and k-Nearest Neighbors (kNN)
模組 12：分類與 k 最近鄰 (kNN)

• Classification is the process of sorting data into categories based on certain features or characteristics.
  分類是根據某些特徵或特性將數據分類的過程。
• Classification in Machine Learning: An Introduction (blog)
  機器學習中的分類：介紹（部落格）
• k-Nearest Neighbors (kNN) is a method that classifies data based on the closest data points to it.
  k-最近鄰居（kNN）是一種根據與其最近的數據點對數據進行分類的方法。
• StatQuest kNN on YouTube: A clear, engaging introduction to kNN algorithms.
  StatQuest kNN on YouTube: 一個清晰且引人入勝的 kNN 演算法介紹。

Module 13: Logistic Regression
模組 13：邏輯回歸

• Logistic Regression is a technique used to predict the probability of an outcome based on input data, often used for binary classification (like yes or no).
  邏輯回歸是一種根據輸入數據預測結果概率的技術，通常用於二元分類（例如是或否）。
• Understanding Logistic Regression in Python (blog)
  理解 Python 中的邏輯回歸 (部落格)

Module 14: Decision Trees
模組 14：決策樹

• Decision Trees are a model that splits data into branches to make decisions or predictions based on features.
  決策樹是一種模型，將數據分割成分支，以根據特徵做出決策或預測。
• Course Recommendation: Decision Trees by StatQuest on YouTube
  課程推薦：StatQuest 的決策樹（Decision Trees）在 YouTube 上

Module 15: Gradient Descent and Optimization
模組 15：梯度下降與優化

• Gradient Descent is an optimization method used to find the best model by minimizing errors through repeated adjustments.
  梯度下降是一種優化方法，用於通過反覆調整來最小化誤差，以找到最佳模型。
• Gradient Descent Visualizations by 3Blue1Brown on YouTube
  3Blue1Brown 在 YouTube 上的梯度下降可視化
• Optimization is the process of adjusting a model to find the best possible solution by reducing errors.
  優化是調整模型以通過減少錯誤來尋找最佳解決方案的過程。
• Introduction to Optimization in Python on DataCamp
  DataCamp 上的 Python 優化入門

Module 16: Classifying Nonlinear Features
模組 16：分類非線性特徵

• Classifying Nonlinear Features involves using techniques that can handle data with complex, non-straightforward patterns to make accurate predictions.
  分類非線性特徵涉及使用能夠處理具有複雜、非直接模式的數據的技術，以進行準確的預測。
• Course Recommendation: Non Linear Classification - Ep. 4 (Deep Learning Fundamentals)on YouTube: Covers nonlinear classification using neural networks and support vector machines.
  課程推薦：非線性分類 - 第 4 集（深度學習基礎）在 YouTube 上：涵蓋使用神經網絡和支持向量機的非線性分類。

Module 17: Practical Application III
模組 17：實用應用 III

1. Customer Churn Prediction using Classification Algorithms:
1. 使用分類算法進行客戶流失預測：

• Goal: Predict customer churn for a subscription-based service (e.g., telecom, streaming).
  目標：預測基於訂閱的服務（例如，電信、串流媒體）的客戶流失。
• Techniques Used:  使用的技術：
• Classification: Use classification algorithms to predict whether a customer will churn or not.
  分類：使用分類算法預測客戶是否會流失。
• KNN: Apply K-Nearest Neighbors to classify customer churn based on similar user behaviors.
  KNN：應用 K-最近鄰居根據相似的用戶行為來分類客戶流失。
• Logistic Regression: Implement logistic regression to model the probability of churn based on customer features.
  邏輯回歸：實現邏輯回歸以根據客戶特徵建模流失的概率。
• Decision Tree: Build decision tree models to identify important factors influencing churn.
  決策樹：建立決策樹模型以識別影響流失的重要因素。
• Optimization: Use gradient descent to optimize model parameters.
  優化：使用梯度下降法來優化模型參數。

2. Handwritten Digit Recognition:
2. 手寫數字識別：

• Goal: Classify images of handwritten digits from the MNIST dataset.
  目標：對 MNIST 數據集中的手寫數字圖像進行分類。
• Techniques Used:  使用的技術：
• Classification: Build a model to classify digits from images.
  分類：建立一個模型來對圖像中的數字進行分類。
• KNN: Use KNN for pattern recognition and classification based on pixel intensity features.
  KNN：使用 KNN 進行基於像素強度特徵的模式識別和分類。
• Logistic Regression: Apply logistic regression to map pixel values to digit probabilities.
  邏輯回歸：應用邏輯回歸將像素值映射到數字概率。
• Gradient Descent & Optimization: Train the model using gradient descent to minimize classification error.
  梯度下降與優化：使用梯度下降訓練模型以最小化分類錯誤。

3. Credit Card Fraud Detection:
3. 信用卡詐騙偵測：

• Goal: Detect fraudulent transactions using historical transaction data.
  目標：使用歷史交易數據檢測欺詐交易。
• Techniques Used:  使用的技術：
• Classification: Classify transactions as either fraudulent or legitimate.
  分類：將交易分類為欺詐或合法。
• Logistic Regression: Use logistic regression to model the probability of fraud.
  邏輯回歸：使用邏輯回歸來建模詐騙的概率。
• Decision Tree: Use decision trees to understand which transaction attributes lead to fraud.
  決策樹：使用決策樹來了解哪些交易屬性導致詐騙。
• Gradient Descent & Optimization: Apply gradient descent for model training to minimize loss.
  梯度下降與優化：應用梯度下降進行模型訓練以最小化損失。

SECTION 5: Advanced Machine Learning Techniques
第 5 節：進階機器學習技術

Module 18: Natural Language Processing
模組 18：自然語言處理

• Description: Learn techniques for processing and analyzing textual data, focusing on tasks like sentiment analysis, text classification, and named entity recognition (NER).
  描述：學習處理和分析文本數據的技術，重點關注情感分析、文本分類和命名實體識別（NER）等任務。
• Course Recommendation:  課程推薦：
• Kaggle’s Natural Language Processing with Python: Practical exercises and tutorials for building NLP models in Python.
  Kaggle 的 Python 自然語言處理：用於在 Python 中構建 NLP 模型的實踐練習和教程。
• Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala (Paid book)
  實用自然語言處理：建立現實世界 NLP 系統的綜合指南，作者：Sowmya Vajjala（付費書籍）

Module 19: Recommendation Systems
模組 19：推薦系統

• Description: Explore collaborative filtering, content-based filtering, and hybrid models used in recommendation systems for personalizing user experiences.
  描述：探索在推薦系統中用於個性化用戶體驗的協同過濾、基於內容的過濾和混合模型。
• Course Recommendation: Recommender Systems Specialization on Coursera: In-depth exploration of various recommendation algorithms and their applications.
  課程推薦：Coursera 上的推薦系統專業化：深入探索各種推薦算法及其應用。

Module 20: Ensemble Techniques
模組 20：集成技術

• Description: Learn how ensemble methods, such as Random Forests and Gradient Boosting, improve model performance by combining multiple weak learners into a stronger one.
  描述：了解集成方法，如隨機森林和梯度提升，如何通過將多個弱學習者結合成一個更強的學習者來提高模型性能。
• Course Recommendation: Kaggle’s Ensemble Learning Technique Tutorial: Practical tutorials for implementing ensemble techniques in machine learning projects.
  課程推薦：Kaggle 的集成學習技術教程：實用教程，用於在機器學習項目中實施集成技術。

Module 21: Deep Neural Networks I
模組 21：深度神經網絡 I

• Description: Understand the foundational concepts of deep neural networks (DNNs), including feedforward networks, backpropagation, and training algorithms.
  描述：了解深度神經網絡（DNNs）的基本概念，包括前饋網絡、反向傳播和訓練算法。
• Course Recommendation: Fast.ai’s Practical Deep Learning for Coders: Hands-on course focused on building deep learning models.
  課程推薦：Fast.ai 的實用深度學習課程：專注於構建深度學習模型的實踐課程。

Module 22: Neural Networks II
模組 22：神經網絡 II

• Description: Explore advanced deep learning topics, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their applications in image and sequence data.
  描述：探索進階深度學習主題，包括卷積神經網絡（CNNs）和循環神經網絡（RNNs），以及它們在圖像和序列數據中的應用。
• Course Recommendation: Coursera’s Convolutional Neural Networks by Andrew Ng : Focuses on CNNs for image recognition tasks.
  課程推薦：Coursera 的卷積神經網絡，由 Andrew Ng 主講：專注於圖像識別任務的 CNN。

Module 23: Introduction to Generative AI
模組 23：生成式人工智慧簡介

• Description: Learn the basics of generative Al, focusing on generative adversarial networks (GANs) and autoencoders, and explore how these techniques can create new data and solve real-world problems.
  描述：學習生成式人工智慧的基本概念，專注於生成對抗網絡（GANs）和自編碼器，並探索這些技術如何創造新數據和解決現實世界的問題。
• Course Recommendation: Coursera’s Generative Adversarial Networks (GANs) Specialization: A hands-on course on building and training GANs.
  課程推薦：Coursera 的生成對抗網絡 (GANs) 專業課程：一個關於構建和訓練 GANs 的實踐課程。

Module 24: Capstone Project
模組 24：畢業專案

1. Personalized Content Recommendation System Using NLP and Deep Neural Networks
1. 使用自然語言處理和深度神經網絡的個性化內容推薦系統

• Goal: Build a recommendation system that suggests articles, blogs, or books based on a user’s reading history and preferences.
  目標：建立一個推薦系統，根據用戶的閱讀歷史和偏好建議文章、博客或書籍。
• Techniques Used:  使用的技術：
• NLP: Use NLP techniques to analyze and extract features from text-based content (e.g., TF-IDF, word embeddings like Word2Vec or BERT) to understand the semantic meaning of articles.
  NLP：使用 NLP 技術分析和提取文本內容的特徵（例如，TF-IDF、像 Word2Vec 或 BERT 的詞嵌入），以理解文章的語義。
• Recommendation Systems: Implement collaborative filtering and content-based filtering to recommend articles based on user behavior and preferences.
  推薦系統：實施協同過濾和基於內容的過濾，根據用戶行為和偏好推薦文章。
• Ensemble Techniques: Use ensemble methods such as Random Forest or Gradient Boosting to combine multiple models for more accurate recommendations.
  集成技術：使用集成方法，如隨機森林或梯度提升，將多個模型結合以獲得更準確的推薦。
• Deep Neural Networks: Build a deep neural network model to learn complex patterns in user behavior and content features to improve the recommendation system’s performance.
  深度神經網絡：建立一個深度神經網絡模型，以學習用戶行為和內容特徵中的複雜模式，以提高推薦系統的性能。
• Generative AI: Use a generative model like GPT to generate personalized content summaries or new article ideas based on the user’s reading pattern.
  生成式人工智慧：使用生成模型如 GPT 根據用戶的閱讀模式生成個性化內容摘要或新的文章創意。

2. Sentiment Analysis and Product Review Summarization Using NLP and Generative AI
2. 使用自然語言處理和生成式人工智慧的情感分析與產品評論摘要

• Goal: Develop a system that performs sentiment analysis on product reviews and automatically generates concise summaries for products.
  目標：開發一個對產品評論進行情感分析並自動生成產品簡明摘要的系統。
• Techniques Used:  使用的技術：
• NLP: Use NLP techniques like tokenization, named entity recognition (NER), and sentiment classification models to analyze customer reviews and extract key sentiments.
  NLP：使用自然語言處理技術，如斷詞、命名實體識別（NER）和情感分類模型，來分析客戶評論並提取關鍵情感。
• Deep Neural Networks: Use pre-trained models such as BERT or GPT for sentiment classification, and fine-tune them for the specific task of sentiment analysis on product reviews.
  深度神經網絡：使用預訓練模型如 BERT 或 GPT 進行情感分類，並對其進行微調以適應產品評論的情感分析特定任務。
• Generative AI: Implement generative models (like GPT) to automatically generate short, meaningful summaries of product reviews while preserving the sentiment and key features.
  生成式人工智慧：實現生成模型（如 GPT），自動生成產品評論的簡短、有意義的摘要，同時保留情感和關鍵特徵。
• Ensemble Techniques: Apply ensemble techniques like stacking to combine multiple models (e.g., logistic regression, decision trees) for sentiment analysis to improve accuracy.
  集成技術：應用集成技術，如堆疊，將多個模型（例如，邏輯回歸、決策樹）結合起來進行情感分析，以提高準確性。
• Recommendation Systems: Enhance the system by recommending products based on users’ sentiment preferences, i.e., recommending highly rated products with positive sentiments.
  推薦系統：通過根據用戶的情感偏好來推薦產品來增強系統，即推薦具有正面情感的高評價產品。

3. AI-Driven Music Playlist Generation and Personalization
3. AI 驅動的音樂播放列表生成與個性化

• Goal: Build an AI system that generates personalized music playlists based on a user’s listening history and mood.
  目標：建立一個根據用戶的聆聽歷史和情緒生成個性化音樂播放列表的人工智慧系統。

- Techniques Used:  - 使用的技術：

• NLP: Use NLP to analyze song lyrics and extract features such as sentiment, theme, or genre for better personalization of music recommendations.
  NLP：使用自然語言處理分析歌曲歌詞，提取情感、主題或類型等特徵，以便更好地個性化音樂推薦。
• Recommendation Systems: Implement a hybrid recommendation system that combines user-based collaborative filtering and content-based filtering to suggest songs based on a user’s music preferences and behavior.
  推薦系統：實現一個混合推薦系統，結合基於用戶的協同過濾和基於內容的過濾，根據用戶的音樂偏好和行為建議歌曲。
• Deep Neural Networks: Develop a deep learning model (such as a neural collaborative filtering model) to learn complex patterns in user preferences and music features.
  深度神經網絡：開發一個深度學習模型（例如神經協作過濾模型）以學習用戶偏好和音樂特徵中的複雜模式。
• Ensemble Techniques: Use ensemble methods like XGBoost or LightGBM to improve recommendation accuracy by combining different models (e.g., content-based, collaborative, and hybrid).
  集成技術：使用集成方法，如 XGBoost 或 LightGBM，通過結合不同模型（例如，基於內容的、協作的和混合的）來提高推薦準確性。
• Generative AI: Leverage a generative AI model to create new songs or remixes based on the user’s preferred music style, genre, and mood, enhancing the playlist experience.
  生成式人工智慧：利用生成式人工智慧模型根據用戶偏好的音樂風格、類型和情緒創作新歌曲或混音，提升播放列表體驗。

SECTION 6: Capstone project
第六部分：畢業專案

Goal  目標

Research Papers  研究論文

• Arxiv Sanity: allows you to browse state-of-the-art, trending research in various AI and machine learning domains.
  Arxiv Sanity：允許您瀏覽各種人工智慧和機器學習領域的最先進、熱門研究。
• Browse State-of-the-Art Trending Research: explore the latest, most impactful papers and developments in Al and machine learning.
  瀏覽最前沿的熱門研究：探索人工智慧和機器學習領域最新、最具影響力的論文和發展。
• Deep Learning Monitor: offers comprehensive resources and papers focused specifically on deep learning.
  深度學習監控：提供專注於深度學習的全面資源和論文。
• Distilled AI List of research papers since 2010: provides a curated collection of key papers that have shaped AI research over the years, offering valuable insights into the evolution of the field.
  自 2010 年以來的精華 AI 研究論文列表：提供了一個精心策劃的關鍵論文集合，這些論文塑造了多年的 AI 研究，為該領域的演變提供了寶貴的見解。

Berkeley's Project Ideas
伯克利的專案想法

1. Train a Decision Tree Model with Desired Hyperparameters Using Scikit-learn: Implement a decision tree model using Scikit-learn. You will learn how to tune hyperparameters to optimize the model’s performance and make predictions based on input features.
   使用 Scikit-learn 訓練具有所需超參數的決策樹模型：使用 Scikit-learn 實現決策樹模型。您將學習如何調整超參數以優化模型的性能並根據輸入特徵進行預測。
2. Plot Decision Boundaries Using Logistic Regression: Apply logistic regression to a classification problem and visualize decision boundaries. This exercise will help you understand the distinction between different types of classifiers and their ability to separate classes in feature space.
   使用邏輯回歸繪製決策邊界：將邏輯回歸應用於分類問題並可視化決策邊界。這個練習將幫助你理解不同類型分類器之間的區別及其在特徵空間中分離類別的能力。
3. Construct a Model Using Classical Time Series Decomposition: This activity will teach you how to decompose a time series into its components (trend, seasonality, and noise). You will use classical techniques to build a model that can forecast future values.
   使用經典時間序列分解構建模型：此活動將教您如何將時間序列分解為其組成部分（趨勢、季節性和噪聲）。您將使用經典技術構建一個可以預測未來值的模型。
4. Import and Clean Messy Data from Real-World Datasets: Practice working with raw, unstructured datasets by cleaning and preparing them for analysis. This exercise includes handling missing values, correcting data types, and dealing with outliers to make the data ready for modeling.
   從真實世界數據集導入和清理雜亂數據：通過清理和準備原始、非結構化數據集以進行分析來進行實踐。這個練習包括處理缺失值、修正數據類型以及處理異常值，以使數據為建模做好準備。
5. Create Histograms and Data Visualizations in Python: Use Python libraries like Matplotlib and Seaborn to create histograms and various other data
   在 Python 中創建直方圖和數據可視化：使用 Python 庫如 Matplotlib 和 Seaborn 創建直方圖和各種其他數據
   visualizations. This will help you communicate data insights effectively through visual representations.
   視覺化。這將幫助您通過視覺表現有效地傳達數據洞察。
6. Perform Computations Between DataFrames Using Set Index and Reset Index: Master pandas DataFrame operations, particularly using the set_index and reset_index methods to perform computations and manipulate data efficiently for analysis.
   使用設置索引和重置索引在數據框之間執行計算：掌握 pandas DataFrame 操作，特別是使用 set_index 和 reset_index 方法高效地執行計算和操作數據以進行分析。
7. Perform String Manipulation in Pandas: Clean and transform textual data using pandas. String manipulation is a critical skill when dealing with real-world data, and you’ll learn how to handle operations like string matching, substitution, and extraction.
   在 Pandas 中執行字串操作：使用 pandas 清理和轉換文本數據。字串操作是處理現實世界數據時的一項關鍵技能，您將學習如何處理字串匹配、替換和提取等操作。
8. Apply Singular Value Decomposition (SVD) to a Specific Dataset: Singular Value Decomposition (SVD) is a powerful matrix factorization technique used for dimensionality reduction. In this exercise, you will apply SVD to reduce the dimensions of a dataset and analyze the resulting components.
   對特定數據集應用奇異值分解（SVD）：奇異值分解（SVD）是一種強大的矩陣分解技術，用於降維。在這個練習中，您將應用 SVD 來降低數據集的維度並分析結果組件。

Utilizing ChatGPT  利用 ChatGPT

Additional Tips:  額外提示：

• Consistency is vital: Dedicate a specific time each day for studying.
  一致性至關重要：每天專門抽出一段時間來學習。
• Take breaks: Avoid burnout by taking short breaks.
  休息：透過短暫的休息來避免倦怠。
• Join online communities: Connect with other learners for support and collaboration.
  加入線上社群：與其他學習者聯繫以獲得支持和合作。
• Build projects: Apply your knowledge by creating small projects.
  建立專案：透過創建小型專案來應用你的知識。
• Stay motivated: Set achievable goals and celebrate your progress.
  保持動力：設定可實現的目標並慶祝你的進步。