Python is a versatile language with libraries like Pandas, NumPy, and Matplotlib for data analysis, visualization, and machine learning. It supports MySQL, NLP, statistical analysis, and deep learning through Keras, making it ideal for comprehensive data science and AI tasks.
Python is a versatile language with powerful libraries for data analysis, visualization, machine learning, and deep learning, making it essential for data science and AI.
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Introduction to Python
Python – Tools, Syntaxes & Data Structures
Python – Dictionaries, Date, and Time
Python – MySQL
Python – Date and Time, Data Wrangling
Pandas Library – Introduction and Data Structures
Python – Data Cleansing
Pandas – Advanced Data Manipulations
Numpy Library – Introduction, Array Manipulations, Mathematical Functions
I/O with Numpy
SKLearn Library
Python – Types of Distributions
Python – Correlation, Chi-Square Test, Linear Regression
Keras Library – Introduction
Python – Word Tokenization, Stemming, and Lemmatization
Matplotlib Library – Introduction, Pyplot API, and Types of Plots
Matplotlib – Histogram Using Matplotlib
Seaborn Library
Python – Data Visualization
Python – Statistical Analysis
Python – Data Wrangling and Data Cleansing
Python – Statistical Analysis and Visualization
Python – Data Wrangling, Data Cleansing
Python – Types of Distribution, Correlation, Chi-Square Test
Python – Linear Regression, Keras Library
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Python is an interpreted, dynamically-typed, high-level language with a focus on readability and simplicity. It supports multiple paradigms, such as procedural, object-oriented, and functional programming.
Lists, tuples, sets, and dictionaries are the primary data structures in Python.
Use datetime.now() from the datetime module.
Use the mysql.connecter library or SQLAlchemy for ORM support.
Pandas is used for data manipulation and analysis, offering data structures like Series and DataFrames.
Numpy is a library for numerical computing in Python, providing support for arrays and matrix operations.
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