Machine learning with Python is a guide to applying machine learning using the Python programming language. It discusses why machine learning is important due to growing data and a shortage of data analysts. Machine learning has applications in astronomy, healthcare, automotive, finance, gaming, and agriculture. The guide also provides an overview of the data science process, which incorporates skills from computer science, statistics, and mathematics to extract knowledge from large datasets and apply insights to solve problems. It discusses the relationships between artificial intelligence, machine learning, deep learning, and data science. Finally, it outlines the steps to learn data science, including choosing a programming language, machine learning algorithms, IDEs, data visualization, and more.

1. Machine learning with Python

2. • Why ?
– Growth of data (28%) vs growth of data analyst(5.7%)
• Applications
:Astronomy,Healthcare,automotive,Finance,Gaming,Agric
ulture
• Data scientist,Data Engineer,Visualization expert,ML
Engineer,ML scientist

3. Data Science
• Data science is an interdisciplinary field focused on extracting knowledge from data sets,
which are typically large , and applying the knowledge and actionable insights from data to
solve problems in a wide range of application domains.
• The field encompasses preparing data for analysis, formulating data science problems,
analyzing data, developing data-driven solutions, and presenting findings to inform high-level
decisions in a broad range of application domains.
• As such, it incorporates skills from computer science, statistics, information science,
mathematics, information visualization, data integration, graphic design, complex systems,
communication and business

4. AI vs ML vs DL vs DS

5. Step by step procedure to learn data science
Data
science
Programming
language
Machine
learning
IDE
Web scraping
Math
Data
visualization
Data Analysis
Python,R,Java
Classification,Regression,Reinforce
ment, DL , Dimesionality
Reduction,Clustering
Pycharm,Jupyter,
Spyder
Beautiful
soup,Scrapy,URLLib
Statistics,Linear
Algebra, Differential
Calculus
Tableau,Power BI ,
MatPlotLib.GGplot,
Seaborn
Feature engineering
,Data wrangling,
EDA

6. Anaconda installation
• https://www.anaconda.com/products/individual
• https://docs.anaconda.com/anaconda/install/windows/
(step by step installation of anaconda in windows)