人工智能和机器学习课程 Artificial intelligence and Machine learning course

学习人工智能，通过应用算法来训练计算机、人类的智能过程

你会学到:
人工智能是什么
什么是机器学习
什么是神经网络
谁来构建预测建模程序

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2 Ch
语言：英语+中英文字幕（云桥网络 机译） |大小解压后:1.51 GB

要求
面向对象编程基础
商业智能基础
Python基础知识

描述
人工智能是通过创建和应用构建在动态计算环境中的算法来模仿人类智能过程的基础。简而言之，人工智能试图让计算机像人类一样思考和行动。

实现这一目标需要三个关键组成部分:

计算系统

数据和数据管理

高级人工智能算法(代码)

期望的结果越像人类，需要的数据和处理能力就越多。

为什么人工智能很重要？

今天，人类和机器产生的数据量远远超过了人类吸收、解释和基于这些数据做出复杂决策的能力。人工智能构成了所有计算机学习的基础，是所有复杂决策的未来。举个例子，大多数人都能想出如何在井字游戏(零和叉)中不输，尽管有255，168个独特的动作，其中46，080个以平局告终。少得多的人会被认为是跳棋的冠军，有超过500 x 1018，或500，000，000，不同的潜在动作。计算机在计算这些组合和排列以得出最佳决策方面效率极高。人工智能(及其机器学习的逻辑进化)和深度学习是商业决策的基础未来。

人工智能的要求

人工智能的应用可以在日常场景中看到，例如金融服务欺诈检测、零售购买预测和在线客户支持交互。这里只是几个例子:

欺诈检测。金融服务业在两个方面使用人工智能。信用申请的初始评分使用人工智能来了解信誉。更先进的人工智能引擎被用来实时监控和检测欺诈支付卡交易。

虚拟客户援助(VCA)。呼叫中心使用VCA来预测和响应人类交互之外的客户询问。语音识别，加上模拟人的对话，是客户服务询问的第一个互动点。更高级别的查询被重定向到人类。

当一个人通过聊天(聊天机器人)在网页上发起对话时，这个人经常与运行专业人工智能的计算机进行交互。如果聊天机器人不能解释或解决这个问题，人类就会介入直接与这个人交流。这些非企业实例被输入机器学习计算系统，以改进人工智能应用程序，用于未来的交互。

机器学习

机器学习自然是人工智能的一个子集。它提供了统计方法和算法，并使机器/计算机能够从它们以前的经验和数据中自动学习，并允许程序相应地改变其行为。

这门课是给谁的
程序员
学生
工程师
学习爱好者
业务负责人
分析专业人员

1.人工智能和机器学习导论
2.探索Python熊猫，上传平面文件数据并执行操作
3.熊猫、Numpy和Matplotlib在探索数值数据中的应用
4.理解线性回归
5.理解逻辑回归
6.主成分分析和K均值聚类
7.理解决策树算法
8.张量流基础
9.MNIST数据集简介
10.深入研究神经网络
11.卷积神经网络
12.使用谷歌初始模型重新训练数据以创建分类器
13.递归神经网络(RNN)

1. Introduction to Artificial Intelligence & Machine Learning
2. Exploring Python Pandas, Uploading Flat file data and perform operations
3. Usage of Pandas, Numpy and Matplotlib for Exploring numerical data
4. Understanding Linear Regression
5. Understanding Logistic Regression
6. Principal Component Analysis (PCA) and K Means Clustering
7. Understanding Decision Tree Algorithm
8. Basics of Tensor Flow
9. Introduction to MNIST Dataset
10. Deep dive into Neural Networks
11. Convolutional Neural Network (CNN)
12. Retrain data to create classifier using Google inception model
13. Recurrent Neural Networks (RNN)

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (3h 28m) | Size: 1.3 GB

Learn Artificial intelligence (AI) and train computer, human intelligence processes through application of algorithms

What you’ll learn:
What id Artificial intelligence
What is Machine learning
What is Neural Network
Who to build program for predictive modelling

Requirements
Basics of Object Oriented Programming
Basics of Business Intelligence
Basics of Python

Description
Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans.

Achieving this end requires three key components:

Computational systems

Data and data management

Advanced AI algorithms (code)

The more humanlike the desired outcome, the more data and processing power required.

Why is artificial intelligence important?

Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. As an example, most humans can figure out how to not lose at tic-tac-toe (noughts and crosses), even though there are 255,168 unique moves, of which 46,080 end in a draw. Far fewer folks would be considered grand champions of checkers, with more than 500 x 1018, or 500 quintillion, different potential moves. Computers are extremely efficient at calculating these combinations and permutations to arrive at the best decision. AI (and its logical evolution of machine learning) and deep learning are the foundational future of business decision making.

Requirement of Artificial intelligence

Applications of AI can be seen in everyday scenarios such as financial services fraud detection, retail purchase predictions, and online customer support interactions. Here are just a few examples:

Fraud detection. The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand creditworthiness. More advanced AI engines are employed to monitor and detect fraudulent payment card transactions in real time.

Virtual customer assistance (VCA). Call centers use VCA to predict and respond to customer inquiries outside of human interaction. Voice recognition, coupled with simulated human dialog, is the first point of interaction in a customer service inquiry. Higher-level inquiries are redirected to a human.

When a person initiates dialog on a webpage via chat (chatbot), the person is often interacting with a computer running specialized AI. If the chatbot can’t interpret or address the question, a human intervenes to communicate directly with the person. These noninterpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions.

Machine Learning

Machine Learning is naturally a subset of AI. It provides the statistical methods and algorithms and enables the machines/computers to learn automatically from their previous experiences and data and allows the program to change its behavior accordingly.

Who this course is for
Programmers
Students
Engineers
Learning lovers
Business Heads
Analytics Professionals
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