Machine Learning in Python (2022)

本课程将帮助您发展机器学习技能，以解决新数字世界中的现实问题。机器学习结合了计算机科学和统计学来分析原始实时数据、识别趋势并做出预测。参与者将探索为企业构建机器学习解决方案的关键技术和工具。您无需具备任何技术知识即可学习此技能。

你会学到什么

定义机器学习的作用及其重要性

了解机器学习的作用

解释什么是统计

学习不同类型的描述统计

解释概率的含义及其重要性

定义概率过程如何发生

讨论目标和数据收集步骤的定义

了解数据准备和数据探索性分析步骤的不同概念

定义什么是监督学习

区分有监督、无监督和强化学习之间的主要区别

了解三类机器学习之间的区别

探索两类监督学习的用法

解释线性回归的重要性

学习不同类型的逻辑回归

了解什么是集成开发环境及其重要性

了解开发人员使用集成开发环境的因素

了解有关如何执行加法操作和关闭 Jupyter Notebook 的最重要因素

在 Python 中应用和使用各种操作

讨论 Python 中的算术运算

识别 Python 中不同类型的内置数据类型

了解Dictionaries-Built-in Data types最重要的注意事项

解释 Python 中操作的用法及其重要性

了解逻辑运算符的重要性

定义不同类型的受控语句

能够创建和编写程序来查找最大数量

…和更多！

内容和概述

您将从机器学习的历史开始；传统编程与机器学习的区别；机器学习有什么作用；机器学习的定义；应用 Apple 排序示例体验；机器学习的作用；机器学习关键术语；统计基本术语；描述性统计——统计类型；描述性统计的类型；什么是推论统计；什么是分析及其类型；概率和现实生活中的例子；概率如何是一个过程；概率观点；基本概率论。

然后您将了解定义目标和数据收集步骤；数据准备和数据探索性分析步骤；建立机器学习模型和模型评估；机器学习过程中的预测步骤；机器如何解决问题-讲座概述；什么是监督学习；什么是无监督学习；什么是强化学习；监督、无监督和强化学习之间的主要区别；机器学习的三类；什么是回归、分类和聚类；监督学习的两类；无监督学习类别；回归、分类和聚类的比较；什么是线性回归；线性回归的优缺点；线性回归的局限性；什么是逻辑回归；线性回归和逻辑回归的比较；逻辑回归的类型；Logistic回归的优缺点；逻辑回归的局限性；什么是决策树及其在机器学习中的重要性；决策树的优缺点。

我们还将介绍什么是集成开发环境；部分集成开发环境；为什么开发人员使用集成开发环境；哪个 IDE 用于机器学习；什么是开源 IDE；什么是 Python；与 Python 一起用于机器学习的最佳 IDE；Anaconda 分发平台和 Jupyter IDE；Jupyter 中的三个重要选项卡；在 Jupyter 中创建新文件夹和笔记本；在笔记本中创建三个变量；如何检查笔记本中的可用变量；如何执行加法操作并关闭 Jupyter Notebook；如何避免 Jupyter Notebook 中的错误；Python的历史；Python的应用；什么是 Python 的变量基础；Python 中的变量命名规则；Python 中的数据类型；Python中的算术运算；Python中的各种操作；Python中的比较操作；Python中的逻辑运算；Python中的身份操作；Python中的会员操作；Python中的按位运算；Python中的数据类型；Python中的运算符；Python中的控制语句；Python中的库；Python中的库；什么是 Scipy 库；什么是熊猫图书馆；什么是 Statsmodel 及其特点；

本课程还将涉及数据可视化和 Scikit Learn；什么是数据可视化；Matplotib 库；海伯恩图书馆；Scikit-learn 库；什么是数据集；数据集的组成部分；数据收集和准备；数据收集是什么意思；理解数据；探索性数据分析；探索性数据分析方法；数据预处理；分类变量；数据预处理技术。

本课程还将讨论什么是线性回归及其用例；线性回归数据集；导入库和加载数据集-线性回归的步骤；去除线性回归的Index Column-Steps；探索预测变量和响应之间的关系；配对图方法说明；Corr和Heatmap方法说明；创建简单的线性回归模型；解释模型系数；使用我们的模型进行预测；模型评估指标；线性回归的实现-讲座概述；在 Jupyter Notebook 中上传数据集；导入库并将数据集加载到数据框中；删除索引列；探索性分析 – 预测和响应的关系；创建线性回归模型；模型系数；做出预测；模型性能评估。

Learn model building, evaluation, algorithms, machine learning concepts, data science PLUS Python coding and libraries

Published 04/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 215 lectures (12h 45m) | Size: 6.6 GB

What you’ll learn
Define what Machine Learning does and its importance
Learn the different types of Descriptive Statistics
Apply and use Various Operations in Python
Explore the usage of Two Categories of Supervised Learning
Learn the difference of the Three Categories of Machine Learning
Understand the Role of Machine Learning
Explain the meaning of Probability and its importance
Define how Probability Process happen
Discuss the definition of Objectives and Data Gathering Step
Know the different concepts of Data Preparation and Data Exploratory Analysis Step
Define what is Supervised Learning
Differentiate Key Differences Between Supervised,Unsupervised,and Reinforced Learning
Explain the importance of Linear Regression
Learn the different types of Logistic Regression
Learn what is an Integrated Development Environment and its importance
Understand the factors why Developers use Integrated Development Environment
Learn the most important factors on How to Perform Addition operation and close Jupyter Notebook
Discuss Arithmetic Operation in Python
Identify the different Types of Built-in-Data Types in Python
Learn the most important considerations of Dictionaries-Built-in Data types
Explain the usage of Operations in Python and its importance
Understand the importance of Logical Operators
Define the different types of Controlled Statements
Be able to create and write a program to find maximum number
Differentiate the different types of range functions in Python
Explain what is Statistics, Probability and key concepts

Requirements
No technical knowledge or experience is required to get going in this course
A basic understanding of the importance of data science will be useful

Description
Get instant access to a 234-page Machine Learning workbook containing all the reference material

Over 12 hours of clear and concise step by step instructions, practical lessons and engagement

35 quizzes and knowledge checks at various stages to test your learning and confirm your growth

Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificate

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 analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn this skill.

What will you learn

Define what Machine Learning does and its importance

Understand the Role of Machine Learning

Explain what is Statistics

Learn the different types of Descriptive Statistics

Explain the meaning of Probability and its importance

Define how Probability Process happens

Discuss the definition of Objectives and Data Gathering Step

Know the different concepts of Data Preparation and Data Exploratory Analysis Step

Define what is Supervised Learning

Differentiate Key Differences Between Supervised, Unsupervised, and Reinforced Learning

Learn the difference between the Three Categories of Machine Learning

Explore the usage of Two Categories of Supervised Learning

Explain the importance of Linear Regression

Learn the different types of Logistic Regression

Learn what is an Integrated Development Environment and its importance

Understand the factors why Developers use Integrated Development Environment

Learn the most important factors on How to Perform Addition operations and close the Jupyter Notebook

Apply and use Various Operations in Python

Discuss Arithmetic Operation in Python

Identify the different types of Built-in-Data Types in Python

Learn the most important considerations of Dictionaries-Built-in Data types

Explain the usage of Operations in Python and its importance

Understand the importance of Logical Operators

Define the different types of Controlled Statements

Be able to create and write a program to find the maximum number

…and more!

Contents and Overview

You’ll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.

Then you will learn about Defining Objectives and Data Gathering Step; Data Preparation and Data Exploratory Analysis Step; Building a Machine Learning Model and Model Evaluation; Prediction Step in the Machine Learning Process; How can a machine solve a problem-Lecture overview; What is Supervised Learning; What is Unsupervised Learning; What is Reinforced Learning; Key Differences Between Supervised,Unsupervised and Reinforced Learning; Three Categories of Machine Learning; What is Regression, Classification and Clustering; Two Categories of Supervised Learning; Category of Unsupervised Learning; Comparison of Regression , Classification and Clustering; What is Linear Regression; Advantages and Disadvantages of Linear Regression; Limitations of Linear Regression; What is Logistic Regression; Comparison of Linear Regression and Logistic Regression; Types of Logistic Regression; Advantages and Disadvantages of Logistic Regression; Limitations of Logistic Regression; What is Decision tree and its importance in Machine learning; Advantages and Disadvantages of Decision Tree.

We will also cover What is Integrated Development Environment; Parts of Integrated Development Environment; Why Developers Use Integrated Development Environment; Which IDE is used for Machine Learning; What are Open Source IDE; What is Python; Best IDE for Machine Learning along with Python; Anaconda Distribution Platform and Jupyter IDE; Three Important Tabs in Jupyter; Creating new Folder and Notebook in Jupyter; Creating Three Variables in Notebook; How to Check Available Variables in Notebook; How to Perform Addition operation and Close Jupyter Notebook; How to Avoid Errors in Jupyter Notebook; History of Python; Applications of Python; What is Variable-Fundamentals of Python; Rules for Naming Variables in Python; DataTypes in Python; Arithmetic Operation in Python; Various Operations in Python; Comparison Operation in Python; Logical Operations in Python; Identity Operation in Python; Membership Operation in Python; Bitwise Operation in Python; Data Types in Python; Operators in Python; Control Statements in Python; Libraries in Python; Libraries in Python; What is Scipy library; What is Pandas Library; What is Statsmodel and its features;

This course will also tackle Data Visualisation & Scikit Learn; What is Data Visualization; Matplotib Library; Seaborn Library; Scikit-learn Library; What is Dataset; Components of Dataset; Data Collection & Preparation; What is Meant by Data Collection; Understanding Data; Exploratory Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing; Categorical Variables; Data Pre-processing Techniques.

This course will also discuss What is Linear Regression and its Use Case; Dataset For Linear Regression; Import library and Load Data set- steps of linear regression; Remove the Index Column-Steps of Linear Regression; Exploring Relationship between Predictors and Response; Pairplot method explanation; Corr and Heatmap method explanation; Creating Simple Linear Regression Model; Interpreting Model Coefficients; Making Predictions with our Model; Model Evaluation Metric; Implementation of Linear Regression-lecture overview; Uploading the Dataset in Jupyter Notebook; Importing Libraries and Load Dataset into Dataframe; Remove the Index Column; Exploratory Analysis -relation of predictor and response; Creation of Linear Regression Model; Model Coefficients; Making Predictions; Evaluation of Model Performance.

Next, you will learn about Model Evaluation Metrics and Logistic Regression – Diabetes Model.

Who are the Instructors?

Samidha Kurle from Digital Regenesys is your lead instructor – a professional making a living from her teaching skills with expertise in Machine Learning. She has joined with content creator Peter Alkema to bring you this amazing new course.

You’ll get premium support and feedback to help you become more confident with finance!

Our happiness guarantee…

We have a 30-day 100% money-back guarantee, so if you aren’t happy with your purchase, we will refund your course – no questions asked!

We can’t wait to see you on the course!

Enrol now, and master Machine Learning!

Peter and Samidha

Who this course is for
Anyone interested in the field of Machine Learning and key concepts
People who want to understand ML and build models in Python