Complete Machine Learning & Data Science with Python| ML A-Z

近年来，自动驾驶汽车、数字助理、机器人工厂员工和智慧城市已经证明智能机器是可能的。人工智能已经改变了零售、制造、金融、医疗保健和媒体等大多数行业，并继续入侵新的领域。每天都有一个新的应用程序、产品或服务揭示它正在使用机器学习来变得更聪明、更好。

人工智能是下一个数字前沿，对商业和社会具有深远的影响。根据财富商业洞察，全球人工智能市场规模预计到 2026 年将达到 2025.7 亿美元。

这门数据科学与机器学习 (ML) 课程不仅基于“动手”实践，还包括几个用例，以便学生了解实际的行业需求和工作文化。这些是在 AI 中开发任何高级应用程序的要求。

本课程包括多个机器学习 (ML) 项目。

1) 项目 – 使用 K 均值聚类的客户细分

2）项目 – 使用机器学习（Python）检测假新闻

3) 项目 COVID-19：使用机器学习的冠状病毒感染概率

4）项目-使用K-means聚类的图像压缩| 使用 K-Means 的颜色量化

MP4 | Video: h264, 1280×720 | Audio: AAC, 44100 Hz
Language: English | Size: 3.16 GB | Duration: 11h 13m

What you’ll learn
Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
Machine learning Concept and Different types of Machine Learning
Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree,K-Nearest Neighbor(KNN) Algorithm,Support Vector Machine Algorithm,Random Forest Algorithm
Feature engineering
Python Basics
Requirements
No previous programming experience needed.
Description
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.

This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI.

In this course several Machine Learning (ML) projects are included.

1) Project – Customer Segmentation Using K Means Clustering

2) Project – Fake News Detection using Machine Learning (Python)

3) Project COVID-19: Coronavirus Infection Probability using Machine Learning

4) Project – Image compression using K-means clustering | Color Quantization using K-Means

This course include topics —

What is Data Science

Describe Artificial Intelligence and Machine Learning and Deep Learning

Concept of Machine Learning – Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement Learning

Python for Data Analysis- Numpy

Working envirnment-

Google Colab

Anaconda Installation

Jupyter Notebook

Data analysis-Pandas

Matplotlib

What is Supervised Machine Learning

Regression

Classification

Multilinear Regression Use Case- Boston Housing Price Prediction

Save Model

Logistic Regression on Iris Flower Dataset

Naive Bayes Classifier on Wine Dataset

Naive Bayes Classifier for Text Classification

Decision Tree

K-Nearest Neighbor(KNN) Algorithm

Support Vector Machine Algorithm

Random Forest Algorithm I

What is UnSupervised Machine Learning

Types of Unsupervised Learning

Advantages and Disadvantages of Unsupervised Learning

What is clustering?

K-means Clustering

Image compression using K-means clustering | Color Quantization using K-Means

Underfitting, Over-fitting and best fitting in Machine Learning

How to avoid Overfitting in Machine Learning

Feature Engineering

Teachable Machine

Python Basics

In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better.

Who this course is for:
Anyone interested in Machine Learning.
Any students in college who want to start a career in Data Science.