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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-tutorial/
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课程名称：机器学习大师班，轻松掌握AI（从零到英雄！！） 课程概述： 欢迎参加“机器学习大师班，轻松掌握AI（从零到英雄！！）”。本课程将带您从初学者成长为机器学习领域的熟练实践者。无论您是完全的新手还是有编程经验的学生，课程都旨在为您提供在机器学习和数据科学领域成功所需的知识与技能。课程涵盖了数据科学、统计学等多个主题，并提供基础理论和实用技术的全面指导。通过逐步教程和真实案例，您不仅会获得知识，还能动手实践构建自己的模型。 课程内容包括： 1. **Python基础与高级概念**：学习Python编程的基本知识，包括装饰器和生成器。同时，探索NumPy和Pandas等重要库，进行高效的数据操作与分析。 2. **机器学习概念**：理解无监督学习与监督学习的核心概念，学习标准差、百分位数和分位数等统计指标。 3. **数据预处理**：学习如何将数据分为测试集和训练集以评估模型，并处理缺失数据，探索欠采样和过采样的技术。 4. **回归分析**：掌握简单线性回归、多元线性回归、支持向量回归（SVR）、决策树回归、随机森林回归和多项式回归等回归技术。 5. **分类**：深入学习分类算法，包括逻辑回归、K-近邻（K-NN）、支持向量机（SVM）、朴素贝叶斯、决策树分类和随机森林分类。 6. **聚类**：掌握K均值聚类艺术，并学习如何确定最佳聚类数。 7. **强化学习**：探索强化学习算法，重点关注上置信界（UCB）方法。 8. **自然语言处理（NLP）**：了解NLP及其在文本分类中的应用，构建自己的文本分类器。 9. **深度学习**：深入学习神经网络、反向传播、数值数据表示和激活函数等内容。 10. **模型选择与提升**：发现模型选择与优化技术，例如K折交叉验证、参数调优与网格搜索，学习增强性能的XGBoost算法。 11. **使用Flask构建Web应用程序与模型部署**：动手实践使用Flask构建基本Web应用程序，学习如何在Web应用中部署您的机器学习模型。 课程旨在为您提供大量实践练习，以确保您能够自信地构建模型。我们相信机器学习在当今数据驱动的世界中至关重要。通过学习本课程，您将获得重要技能，提升作为数据科学家、机器学习工程师或计算机视觉专家的职业前景。 选择本课程的理由： - **全面覆盖**：从Python基础到高级机器学习技术，全方位构建坚实基础。 - **实用方法**：提供实践练习和真实案例，帮助应用所学知识。 - **经验丰富的讲师**：八年教学经验，帮助超140,000名学生，课程将以清晰简洁的方式指导您。 - **高质量教学**：注重简单和逐步学习，将复杂概念以易于理解的方式传授。 无论您是初学者还是经验丰富的程序员，这门课程都会为您提供在机器学习领域成功所需的知识和实践技能。购买课程后，您可在30天内无条件退款，确保您没有任何风险。 现在就加入我们，开启您的机器学习之旅吧！
Welcome to the MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)In this course, we will take you on a journey from a beginner to a proficient practitioner in the exciting field of Machine Learning. Whether you are a beginner or have prior programming experience, this course is designed to equip you with the knowledge and skills needed to excel in machine learning and data science. Whether you're interested in data science, or statistics, or simply want to kick-start your Machine Learning journey, this course covers all the essential theory and practical techniques you need to succeed. With step-by-step tutorials and real-life examples, you'll not only gain knowledge but also get hands-on practice building your own models. Here's a breakdown of what you'll learn in each section of the course:Course Overview:Section 1 - Python Basics and Advanced Concepts:Learn the fundamentals of Python programming, including decorators and generators.Explore essential libraries such as NumPy and Pandas for efficient data manipulation and analysis.Section 2 - Machine Learning Concepts:Understand the core concepts of Unsupervised and Supervised learning.Dive into statistical measures like standard deviation, percentiles, and quantiles.Master descriptive statistics such as mean, mode, and median.Section 3 - Data Preprocessing:Learn how to split data into test and train sets for model evaluation.Handle missing data and explore techniques like under and oversampling.Section 4 - Regression:Build a strong foundation in regression analysis, including simple linear regression, multiple linear regression, SVR, decision tree regression, random forest regression, and polynomial regression.Section 5 - Classification:Gain expertise in classification algorithms, including logistic regression, K-nearest neighbors (K-NN), support vector machines (SVM), naive Bayes, decision tree classification, and random forest classification.Section 6 - Clustering:Master the art of clustering with K-means clustering and learn to determine the optimal number of clusters.Section 7 - Reinforcement Learning:Explore reinforcement learning algorithms, focusing on the Upper Confidence Bound (UCB) approach.Section 8 - Natural Language Processing (NLP):Gain an introduction to NLP and its applications in text classification using machine learning.Build your own text classifier using the techniques learned.Section 9 - Deep Learning:Delve into the fascinating world of deep learning, including neural networks, backpropagation, data representation using numbers, and activation functions.Section 10 - Model Selection & Boosting:Discover techniques for model selection and optimization, such as k-fold cross-validation, parameter tuning, and grid search.Learn about the powerful XGBoost algorithm for boosting performance.Section 11 - Web Application using Flask and Model Deployment:Get hands-on experience in building a basic web application using Flask.Learn how to deploy your machine learning models in a web application.You'll also cover essential topics like feature selection, visualization, evaluation techniques, and many more.Moreover, the course is packed with practical exercises that are based on real-life examples to reinforce your learning and enable you to build your own models confidently. 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If you ever find the content confusing or need clarification, our experienced instructor will be available to address your doubts promptly.Topics You'll Learn:Effective and efficient machine learning methods which are executed devoid of any issuesIssues that can be solved through Machine LearningHow Machine Learning can be used to process functionsUse Python for Machine LearningPercentiles, moment and quantilesLearn to utilize Matplotlib for Python plottingLearn to utilize Seaborn for measurable plotsLearn Advance Mathematics for Machine LearningUnderstand matrix multiplication, Matrix operations, and scalar operationsUse Pair plot and limitationsImplement Identity matrix, matrix inverse properties, transpose of a matrix, and Vector multiplicationImplement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest RegressionAdaBoost and XGBoost regressor, SVM (regression) Background, SVR under PythonML Concept-k-Fold validation, GridSearchClassification-k-nearest neighbours algorithm(KNN)Gaussian Naive Bayes under Python & visualization of modelsLearn evaluation techniques using curves (ROC, AUC, PR, CAP)Implement machine learning algorithmsModel Deployment on Flask WebApplicationNatural Language Processing(NLP)Deep LearningAnd many more interesting topics.Why is Machine Learning Important?Machine learning has become crucial in today's data-driven world. 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