|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-a-z-with-python-with-project-beginner/
课程评论：没有评论
**Python 数据科学与机器学习入门：从零到精通** 本课程是为初学者设计的全面 Python 数据科学与机器学习导论。旨在帮助您理解人工智能 (AI) 和机器学习 (ML) 的核心概念，解锁数据中蕴藏的强大洞察力，并为您在数据科学领域开创高薪职业生涯奠定坚实基础。 **课程亮点：** * **Python 基础及库精通：** 从 Python 基础语法、函数、数据结构（数组、数据框）入手，系统学习 Pandas、NumPy、Matplotlib 和 Seaborn 等关键数据科学库，掌握数据处理、分析和可视化的核心技能。 * **统计学理论与实践：** 深入浅出地讲解描述性统计、概率论、条件概率、假设检验、推断性统计以及常见的概率分布（二项分布、泊松分布、正态分布），并将统计学知识与 Python 实践相结合。 * **机器学习算法详解：** * **监督学习：** 掌握线性回归、非线性回归、K-NN 分类、支持向量机 (SVM) 等回归和分类算法，并学习模型评估指标。 * **无监督学习：** 学习 K-Means 聚类、层次聚类等聚类算法，以及主成分分析 (PCA) 等降维技术。 * **集成学习：** 探索决策树、Bagging、Random Forests 和 Boosting 等集成技术，提升模型性能。 * **模型优化与应用：** 深入理解特征工程、模型选择、超参数调优（Grid Search CV, Random Search CV, K-Fold Cross-Validation）、正则化等关键环节，构建高效的机器学习流水线。 * **进阶主题探索：** 学习推荐系统（协同过滤、基于内容的推荐、混合模型）、时间序列预测（ARIMA 模型）以及模型部署（Kubernetes）等热门应用。 * **项目驱动学习：** 通过实践性的 Jupyter Notebooks、代码示例和最终的 Capstone 项目，将理论知识转化为实际项目经验。 * **实战经验分享：** 讲师将分享在实际数据科学和机器学习项目中遇到的挑战与解决方案，帮助您建立解决真实世界问题的能力。 * **关注伦理：** 课程特别强调深度学习的伦理道德问题，为您提供全面的行业视角。 **学习成果：** 完成本课程后，您将能够： * 熟练运用 Python 及相关库进行数据分析和预处理。 * 理解并应用多种机器学习算法解决实际问题。 * 评估和优化机器学习模型的性能。 * 构建和部署机器学习解决方案。 * 为数据科学和机器学习领域的职业发展做好充分准备。 本课程以通俗易懂的语言讲解概念，避免复杂的数学推导，侧重于实际应用和操作。立即加入，开启您的数据科学与机器学习之旅！
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!Machine Learning (Complete course Overview)FoundationsIntroduction to Machine LearningIntroApplication of machine learning in different fields.Advantage of using Python libraries. (Python for machine learning).Python for AI & MLPython BasicsPython functions, packages, and routines.Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples)Jupyter notebook- installation & functionPandas, NumPy, Matplotib, SeabornApplied StastisticsDescriptive statisticsProbability & Conditional ProbabilityHypothesis TestingInferential StatisticsProbability distributions - Types of distribution - Binomial, Poisson & Normal distributionMachine LearningSupervised LearningMultiple variable Linear regressionRegressionIntroduction to RegressionSimple linear regressionModel Evaluation in Regression ModelsEvaluation Metrics in Regression ModelsMultiple Linear RegressionNon-Linear RegressionNaïve bayes classifiersMultiple regressionK-NN classificationSupport vector machinesUnsupervised LearningIntro to ClusteringK-means clusteringHigh-dimensional clusteringHierarchical clusteringDimension Reduction-PCAClassificationIntroduction to ClassificationK-Nearest NeighboursEvaluation Metrics in ClassificationIntroduction to decision tressBuilding Decision TressInto Logistic regressionLogistic regression vs Linear RegressionLogistic Regression trainingSupport vector machineEnsemble TechniquesDecision TreesBaggingRandom ForestsBoostingFeaturization, Model selection & TuningFeature engineeringModel performanceML pipelineGrid search CVK fold cross-validationModel selection and tuningRegularising Linear modelsBootstrap samplingRandomized search CVRecommendation SystemsIntroduction to recommendation systemsPopularity based modelHybrid modelsContent based recommendation systemCollaborative filteringAdditional ModulesEDAPandas-profiling libraryTime series forecastingARIMA ApproachModel DeploymentKubernetesCapstone ProjectIf you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!Our Learner's Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of "The ethics of deep learning" is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course.
