2024 Master class on Data Science using Python A-Z for ML

Python NumPy, Pandas, Matplotlib and Seaborn for Knowledge Evaluation, Knowledge Science and ML. Pre-machine studying Evaluation.

What you’ll study

College students will learn to create and manipulate arrays, carry out mathematical operations on arrays, and use features corresponding to sorting, looking, and statistics

College students will learn to create and manipulate Sequence and Knowledge Frames.

College students will learn to create plots and charts, customise the looks of visualizations, and add annotations and labels.

NumPy, Pandas, and Matplotlib will sometimes train college students the best way to use these instruments to research and visualize information.

Description

Welcome to 2023 Grasp class on Knowledge Science utilizing Python.

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NumPy is a number one scientific computing library in Python whereas Pandas is for information manipulation and evaluation. Additionally, study to make use of Matplotlib for information visualization. Whether or not you are attempting to enter Knowledge Science, dive into machine studying, or deep studying, NumPy and Pandas are the highest Modules in Python it is best to perceive to make the journey clean for you. On this course, we’re going to begin from the fundamentals of Python NumPy and Pandas to the superior NumPy and Pandas. This course offers you a strong understanding of NumPy, Pandas, and their features.

On the finish of the course, it is best to have the ability to write advanced arrays for real-life initiatives, manipulate and analyze real-world information utilizing Pandas.

WHO IS THIS COURSE FOR?

√ This course is for you if you wish to grasp the in-and-out of NumPy, Pandas, and information visualization.

√ This course is for you if you wish to construct real-world purposes utilizing NumPy or Panda and visualize them with Matplotlib and Seaborn.

√ This course is for you if you wish to study NumPy, Pandas, Matplotlib and Seaborn for the primary time or get a deeper data of NumPy and Pandas to extend your productiveness with deep and Machine studying.

√ This course is for you if you’re coming from different programming languages and need to study Python NumPy and Pandas quick and realize it very well.

√ This course is for you if you’re bored with NumPy, Pandas, Matplotlib and Seaborn programs which can be too temporary, too easy, or too difficult.

√ This course is for you if it’s important to get the prerequisite data to understanding Knowledge Science and Machine Studying utilizing NumPy and Pandas.

√ This course is for you if you wish to study NumPy and Pandas by doing thrilling real-life challenges that may distinguish you from the group.

√ This course is for you if plan to go an interview quickly.

English

language

Content material

BONUS : Python Crash Course

Variables in Python

Conditionals & If assertion

Instance for If assertion

If else assertion

Instance of If else assertion

Nested If assertion

Instance for Nested If assertion

Elif assertion

Instance for Elif assertion

Whereas loop

Instance of whereas loop

For Loop

Instance of For Loop

Break & Proceed Assertion

Introduction to containers

Creating and accessing lists in Python

Record indexing and slicing

Working with Record strategies

Working with operators on lists

Record Comprehension

Tuple : definition

Tuples

Tuple Indexing & Slicing

Manipulating Tuples

Unpacking Tuples

Units

Dictionaries

Fundamentals of dictionary

Accessing dictionary

len, str & kind features in dictionary

Capabilities in python

Instance program1 on Capabilities

Instance program2 on features

Knowledge Dealing with utilizing Numpy

Introduction to modules in python

Creating & Displaying 1D array

Understanding 1D array Index

Creating Array of 0’s and Array of 1’s

Sorting components in 1D array

Slicing a 1D array

Mathematical Operations on Array

Looking out a component in a Array

Filtering an array

Checking whether or not given array is empty or not ?

Creating & Displaying 2D array

ndim Attribute

Dimension Attribute

Form and reshape of array

Creating an Id Matrix

arange()

linspace()

Random array

Random matrix

Making a diagonal matrix

Flatten a Matrix

Computing Hint of a Matrix

Discovering Transpose of a Matrix

Damaging indexing to entry components in a 2D array

Knowledge Dealing with utilizing Pandas

Introduction to Pandas

Working with sequence in Pandas

Combining sequence with Numpy

Discovering variety of components in a sequence

Computing imply, max and min in a sequence

Sorting a Sequence

Displaying Distinctive values in a Sequence

Abstract of sequence statistics

Creating DataFrame From Sequence

Creating DataFrame from Record of Dictionaries

Knowledge Body entry utilizing row-wise and column-wise.

Add, Rename and Delete Columns in a Knowledge Body

Deleting rows and cols utilizing drop()

Boolean Indexing in DataFrames

Concatenating DataFrames

Knowledge Visualization utilizing Matplotlib in Python

Introduction to Matplotlib

Creating Line Graph

Creating Bar Graph

Creating Scatter Graph

Creating Histogram Graph

Creating Pie Chart

Creating 3D Plot

Creating 3D Line graph

Knowledge Visualization utilizing Seaborn in Python

Understanding a pattern Dataset (Downloadable)

Introduction to Seaborn

Swarm Plot

Violin Plot

Aspect Grids

Heatmap

Drawback Fixing Assignments

Initiatives

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College students will learn to create and manipulate arrays, carry out mathematical operations on arrays, and use features corresponding to sorting, looking, and statistics

College students will learn to create and manipulate Sequence and Knowledge Frames.

College students will learn to create plots and charts, customise the looks of visualizations, and add annotations and labels.