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所在平台: Udemy |
课程主页: https://www.udemy.com/course/industry-level-machine-learning-projects/
课程评论：没有评论
Coursera 课程：15个机器学习项目 **课程概述：** 本课程基于15个真实的机器学习项目，旨在帮助您掌握在机器学习行业中应用的实际项目。通过学习，您将为开展真实的机器学习项目打下坚实基础，并向成为数据科学领域的专家迈出重要一步。 **您将学到：** * 掌握全面的机器学习工具集，以应对大多数现实世界的问题。 * 理解各种回归、分类及其他机器学习算法的性能指标（如R-squared, MSE, accuracy, confusion matrix, precision, recall等）及其适用场景。 * 学会通过bagging、boosting或stacking组合多个模型。 * 利用无监督机器学习算法（如Hierarchical clustering, k-means clustering等）深入理解数据。 * 在Jupyter (IPython) notebook, Spyder及各类IDE中进行开发。 * 借助Matplotlib和Seaborn进行有效的可视化沟通。 * 学习特征工程，以提升算法预测性能。 * 掌握train/test, K-fold及Stratified K-fold交叉验证等方法，用于选择模型和预测模型在未见数据上的表现。 * 应用SVM进行手写识别和通用分类问题。 * 使用决策树预测员工离职情况。 * 在零售购物数据集中应用关联规则。 * 以及更多实用技术！ **课程特色：** * **零基础入门：** 无需机器学习基础。虽然基本的Python经验会有帮助，但并非必需，课程会提供所有代码并逐行讲解，并在问答区提供友好支持。 * **行业级项目：** 学习并实践多个业界真实级别的机器学习项目。 * **技能提升：** 增强数据科学技能，为简历增添亮点，并学习如何部署机器学习模型。 * **职业发展：** 帮助您抓住机器学习浪潮，享受数据科学家的高薪。 * **持续支持：** 获得讲师的持续支持，确保您最大化课程价值。 **目标人群：** * 希望利用机器学习解决实际问题的人。 * 希望通过项目经验提升竞争力的人。 * 希望学习模型部署的人。 * 希望成为机器学习工程师的人。 **立即报名，成为一名机器学习工程师！**
This course is based on15 real life machine learning projects- You will work on 15 interesting projects which are used in machine learning industry.My course provides a foundation to carry out real life machine learning projects. By taking this course, you are taking an important step forward in your data science journey to become an expert in harnessing the power of real projects.ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT INDUSTRY LEVEL MACHINE LEARNING PROJECTSDo you want to harness the power of machine learning?Are you looking to gain an edge by adding cool projects in your resume?Do you want to learn how to deploy a machine learning model?Gaining proficiency in machine learning can help you harness the power of the freely available data and information on the world wide web and turn it into actionable insightsInside the course, you'll learn how to:Gain complete machine learning tool sets to tackle most real world problemsUnderstand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.Combine multiple models with by bagging, boosting or stackingMake use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your dataDevelop in Jupyter (IPython) notebook, Spyder and various IDECommunicate visually and effectively with Matplotlib and SeabornEngineer new features to improve algorithm predictionsMake use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen dataUse SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionApply the association rule to retail shopping datasetsAnd much much more!No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q & A area.Make This Investment in YourselfIf you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!Take this course and become a machine learning engineer!In addition to all the above, you'll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!ENROLL NOW:)
