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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-data-science-in-python/
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课程名称：初学者的Python机器学习与数据科学 课程概述：本课程提供69页的机器学习工作手册以及超过9小时的清晰逐步指导、实践课程和互动环节。学员可以在课程中介绍自己并表达学习目标，并在学习进程的25%、50%、75%和100%时进行鼓励和庆祝，最终获得证书。通过本课程，学员将培养解决数字世界实际问题的机器学习技能，结合计算机科学和统计学分析实时原始数据，识别趋势并进行预测。此外，课程不要求任何技术知识即可学习。 学习内容： 1. 机器学习概述 2. 有监督机器学习 3. 无监督机器学习 4. 半监督机器学习 5. 有监督学习的类型：分类与回归 6. 无监督学习的类型：聚类与关联 7. 数据收集与准备 8. 模型选择、训练与评估 9. 高级参数调节（HPT）及预测 10. 数据预处理步骤与Python库 此外，课程还包括： - 特征选择与特征缩放 - 训练与测试数据集 - 机器学习应用及其过程 - Python、Java、R与C++的基础知识 - Jupyter Notebook的使用及Python数学基础 讲师团队： 课程由Tech 100的首席讲师Allah Dittah教授主讲，他在机器学习领域具有丰富经验。与他合作的是内容创作者Peter Alkema。 我们期待在课程中见到你，抓紧时间报名，掌握机器学习技能！
Get instant access to a 69-page Machine Learning workbook containing all the reference materialOver 9 hours of clear and concise step-by-step instructions, practical lessons, and engagementIntroduce yourself to our community of students in this course and tell us your goalsEncouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificateWhat will you get from doing this course?This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses. You don't need to have any technical knowledge to learn these skills.What will you learn:What is Machine LearningSupervised Machine LearningUnsupervised Machine LearningSemi-Supervised Machine LearningTypes of Supervised Learning: ClassificationRegressionTypes of Unsupervised Learning: ClusteringAssociationData CollectionData PreparingSelection of a ModelData Training and EvaluationHPT in Machine LearningPrediction in MLDPP in MLNeed of DPPSteps in DPPPython LibrariesMissing, Encoding, and Splitting Data in MLPython, Java, R,and C ++How to install python and anaconda?Interface of Jupyter NotebookMathematics in PythonEuler's Number and VariablesDegree into Radians and Radians into Degrees in PythonPrinting Functions in PythonFeature Scaling for MLHow to Select Features for MLFilter MethodLDA in MLChi-Square MethodForward SelectionTraining and Testing Data Set for MLSelection of Final ModelML ApplicationsPractical Skills in ML: MasteryProcess of MLWhat is Extension in MLML TradeoffML Variance ErrorLogistic RegressionData VisualizationPandas and Seaborn-Library for ML...and more!Contents and OverviewYou'll start with the What is Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Example of Supervised Machine Learning; Example of Un-Supervised Machine Learning; Example of Semi-Supervised Machine Learning; Types of Supervised Learning: Classification; Regression; Types of Unsupervised Learning: Clustering; Association.Then you will learn about Data Collection; Data Preparation; Selection of a Model; Data Training and Evaluation; HPT in Machine Learning; Prediction in ML; DPP in ML; Need of DPP; Steps in DPP; Python Libraries; Missing, Encoding, and Splitting Data in ML.We will also cover Feature Scaling for ML; How to Select Features for ML; Filter Method; LDA in ML; Chi Square Method; Forward Selection; Training and Testing Data Set for ML; Selection of Final Model; ML Applications; Practical Skills in ML: Mastery; Process of ML; What is Extension in ML; ML Tradeoff; ML Variance Error; What is Regression; Logistic Regression.This course will also tackle Python, Java, R,and C ++; How to install python and anaconda?; Interface of Jupyter Notebook; Mathematics in Python; Euler's Number and Variables; Degree into Radians and Radians into Degrees in Python; Printing Functions in Python.This course will also discuss Random Selection; Random Array in Python; Random Array and Scattering; Scattering Plot; Jupyter Notebook Setup and Problem; Random Array in Python; Printing Several Function in Python; Exponential and Logarithmic Function in Python.Next, you will learn about Simple Line Graph with Matplotlib; Color Scheme with Matplotlib; Dot and Dashed Graph; Scattering 1-Data visualization; Labelling-Data Visualization; Color Processing-Data Visualization; Seaborn Scatter Plot; Import DataFrame by Pandas.Who are the Instructors?Allah Dittah from Tech 100 is your lead instructor - a professional making a living from his teaching skills with expertise in Machine Learning. He has joined with content creator Peter Alkema to bring you this amazing new course.We can't wait to see you on the course!Enrol now, and master Machine Learning!Peter and Allah
