完整的数据科学与Python机器学习课程 (英文)

完整的数据科学与机器学习课程

发布日期：2024年5月
MP4 | 视频：h264, 1920×1080 | 音频：AAC, 44.1 KHz
语言：英语 | 大小：1.23 GB | 时长：4小时12分钟

学习完整的数据科学与机器学习课程

您将学到什么：

掌握数据科学和机器学习的基本概念、技术和工具。
通过Python编程及其库获得数据操作、分析和可视化的实践经验。
使用各种机器学习算法和技术构建和评估预测模型。
需求：

已安装Python
课程描述：
课程标题：完整的数据科学与机器学习课程
课程描述：欢迎来到“完整的数据科学与机器学习课程”！在这个全面的课程中，你将开始掌握数据科学和机器学习的基础，从数据预处理和探索性数据分析到构建预测模型并将它们部署到生产环境。无论你是初学者还是经验丰富的专业人士，这门课程都将为你提供在动态的数据科学和机器学习领域成功所需的知识和技能。

课程概览：

数据科学与机器学习简介：
理解数据科学和机器学习的原则和概念。
探索数据科学在各行业的实际应用和用例。
数据科学的Python基础：
学习Python编程语言及其在数据科学中的库的基础，包括NumPy、Pandas和Matplotlib。
掌握使用Python进行数据操作、分析和可视化技术。
数据预处理和清洗：
理解数据预处理和清洗在数据科学工作流中的重要性。
学习处理数据集中缺失数据、异常值和不一致的技术。
探索性数据分析（EDA）：
进行探索性数据分析，以洞察数据中的潜在模式和关系。
使用统计方法和可视化工具可视化数据分布、相关性和趋势。
特征工程和选择：
创造新特征并转换现有特征以提高模型性能。
使用特征重要性排名和降维技术选择相关特征。
模型构建和评估：
使用机器学习算法构建预测模型，如线性回归、逻辑回归、决策树、随机森林和梯度提升。
使用适当的指标和技术评估模型性能，包括交叉验证和超参数调整。
高级机器学习技术：
深入学习高级机器学习技术，如支持向量机（SVM）、神经网络和集成方法。
模型部署和生产化：
使用容器化和云服务将训练好的机器学习模型部署到生产环境。
监控模型在生产中的性能、可扩展性和可靠性，并进行必要的调整。
现在就报名，用完整的数据科学与机器学习课程释放数据科学和机器学习的全部潜力！

Complete Data Science & Machine Learning Course

Published 5/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.23 GB | Duration: 4h 12m

Learn Complete Data Science & Machine Learning Course

What you’ll learn
Master the essential concepts, techniques, and tools of data science and machine learning.
Acquire hands-on experience with Python programming and its libraries for data manipulation, analysis, and visualization.
Build and evaluate predictive models using a variety of machine learning algorithms and techniques.
Complete Data Science & Machine Learning Course

Requirements
python installed

Description
Course Title: Complete Data Science and Machine Learning CourseCourse Description:Welcome to the “Complete Data Science and Machine Learning Course”! In this comprehensive course, you will embark on a journey to master the fundamentals of data science and machine learning, from data preprocessing and exploratory data analysis to building predictive models and deploying them into production. Whether you’re a beginner or an experienced professional, this course will provide you with the knowledge and skills needed to succeed in the dynamic field of data science and machine learning.Class Overview:Introduction to Data Science and Machine Learning:Understand the principles and concepts of data science and machine learning.Explore real-world applications and use cases of data science across various industries.Python Fundamentals for Data Science:Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.Master data manipulation, analysis, and visualization techniques using Python.Data Preprocessing and Cleaning:Understand the importance of data preprocessing and cleaning in the data science workflow.Learn techniques for handling missing data, outliers, and inconsistencies in datasets.Exploratory Data Analysis (EDA):Perform exploratory data analysis to gain insights into the underlying patterns and relationships in the data.Visualize data distributions, correlations, and trends using statistical methods and visualization tools.Feature Engineering and Selection:Engineer new features and transform existing ones to improve model performance.Select relevant features using techniques such as feature importance ranking and dimensionality reduction.Model Building and Evaluation:Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and gradient boosting.Evaluate model performance using appropriate metrics and techniques, including cross-validation and hyperparameter tuning.Advanced Machine Learning Techniques:Dive into advanced machine learning techniques such as support vector machines (SVM), neural networks, and ensemble methods.Model Deployment and Productionization:Deploy trained machine learning models into production environments using containerization and cloud services.Monitor model performance, scalability, and reliability in production and make necessary adjustments.Enroll now and unlock the full potential of data science and machine learning with the Complete Data Science and Machine Learning Course!

Overview
Section 1: Introduction To Complete Data Science & Machine Learning Course

Lecture 1 Introduction To Course

Section 2: Complete Python Programming Course

Lecture 2 Python Complete Course Introduction

Lecture 3 Python Class 1 : Introduction To Python

Lecture 4 Python Class 2 : Setting Python Environment

Lecture 5 Python Class 3 : Introduction To Variables

Lecture 6 Python Class 4 : Introduction To Keywords

Lecture 7 Python Class 5 : Introduction To Datatypes

Lecture 8 Python Class 6 : ID Function

Lecture 9 Python Class 7 : Arithmetic Operator

Lecture 10 Python Class 8 : Logical Operator

Lecture 11 Python Class 9 : Comparison Operator

Lecture 12 Python Class 10 : Bitwise Operator

Lecture 13 Python Class 11 : Membership Operator

Lecture 14 Python Class 12 : Identity Operator

Lecture 15 Python Class 13 : Conditional Statements

Lecture 16 Python Class 14 : For Loop and Range Function

Lecture 17 Python Class 15 : While Loops

Lecture 18 Python Class 16 : Break and Continue

Lecture 19 Python Class 17 : Function

Lecture 20 Python Class 18 : Try Except Finally Blocks

Lecture 21 Python Class 19 : String and Functions

Lecture 22 Python Class 20 : List and Functions

Lecture 23 Python Class 21 : Tuple and Functions

Lecture 24 Python Class 22 : Dictionary and Functions

Lecture 25 Python Class 23 : Class and Object

Lecture 26 Python Class 24 : Class Methods

Lecture 27 Python Class 25 : Inheritance and its types

Lecture 28 Python Class 26 : Polymorphism and its types

Lecture 29 Python Class 27 : Encapsulation and Access Modifiers

Lecture 30 Python Class 28 : Abstraction

Lecture 31 Python Class 29 : Mini Project

Section 3: Complete Data Science Course

Lecture 32 Complete Data Science Course

Lecture 33 Numpy Complete Course

Lecture 34 Numpy Class 1 : Import and Install

Lecture 35 Numpy Class 2 : Array and its Types

Lecture 36 Numpy Class 3 : Datatypes

Lecture 37 Numpy Class 4 : NDIM Function

Lecture 38 Numpy Class 5 : ARANGE Function

Lecture 39 Numpy Class 6 : CONCATENATE Function

Lecture 40 Numpy Class 7 : NDMIN Function

Lecture 41 Numpy Class 8 : NDITER Function

Lecture 42 Numpy Class 9 : All Functions

Lecture 43 Pandas Class 1 : Import Dataset

Lecture 44 Pandas Class 2 : Head & Tail Function

Lecture 45 Pandas Class 3 : Info Function

Lecture 46 Pandas Class 4 : Drop na Function

Lecture 47 Pandas Class 5 : Fill na Function

Lecture 48 Pandas Class 6 : Drop Duplicates Function

Lecture 49 Pandas Class 7 : Replace Values Function

Lecture 50 Matplotlib Class 1 : Import Dataset

Lecture 51 Matplotlib Class 2 : Show Function

Lecture 52 Matplotlib Class 3 : Marker Function

Lecture 53 Matplotlib Class 4 : Xlabel Ylabel Function

Lecture 54 Matplotlib Class 5 : Title Function

Lecture 55 Matplotlib Class 6 : Linestyle Linewidth Function

Lecture 56 Matplotlib Class 7 : Barplot

Section 4: Complete Machine Learning Course

Lecture 57 Complete Machine Learning Introduction

Lecture 58 Machine Learning Class 1 : Linear Regression

Lecture 59 Machine Learning Class 2 : Logistics Regression

Lecture 60 Machine Learning Class 3 : Support Vector Machine

Lecture 61 Machine Learning Class 4 : KNN

Lecture 62 Machine Learning Class 5 : K Means Clustering

Lecture 63 Machine Learning Class 6 : Naive Bayes

Lecture 64 Machine Learning Class 7 : Decision Tree Classifier

Lecture 65 Machine Learning Class 8 : Random Forest

Students and professionals interested in pursuing a career in data science, machine learning, or artificial intelligence.,Professionals seeking to enhance their skills and stay competitive in the rapidly evolving field of data science and machine learning.

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