A-Z Python Bootcamp- Basics To Data Science (50+ Hours)

A-Z Python Bootcamp will help you learn Python basics by letting you practice Basic syntax, Regular Expression, Data structure and algorithm, and API. This course is designed for experienced programmers looking to expand their career choices by learning Python and total newbies who have never programmed before.

Large corporations like Google and Amazon utilize Python in mission-critical programs like Google Search, making it one of the most well-liked programming languages in the world.

A-Z Python Bootcamp- Basics To Data Science (50+ Hours) Overview

You can learn Python programming and code with confidence after enrolling in this course. You will gain a better understanding of Python's applications as a result. Get a career in Silicon Valley by training to be a junior Python programmer.

Get access to all the course's codes. This course will have 80+ videos that cover the fundamental concepts that a programmer at the beginning level should understand.

This course will be updated frequently so that beginners can learn more. For the following two years, I guarantee that at least one video portion will be added each quarter.

The objective of the Python basic content:

• Giving confidence that any student can be a programmer.
• Detailed Installation process
• Covers syntax in Python
• Decision-making and loops
• Python basics like Data types, functions, and Modules
• Excel Operation
• Python file handling
• Regular Expression
• Programming with OOPS Concept

This course will teach you Python in a hands-on way, and each lecture is accompanied by a complete screencast of coding and an associated code notebook! Learn in the way that works best for you!

The course will assist you in facilitating the handling of files and data from many sources.

Sorting and searching, divide and conquer, greedy algorithms, and dynamic programming are just a few examples of core algorithmic techniques and concepts covered in the course.

You will learn a lot of theory, including how data sorting aids in searching. How to divide a complicated problem into manageable chunks and tackle them in a greedy manner.

Objective of the Python data structure content:

• Recursion
• Algorithm run time analysis
• Arrays
• Stack
• Linked list
• Data Structure
• Binary Tree
• Binary Search Tree
• AVL Tree
• Heap tree
• Queue
• Sorting
• Hash Table
• Graph Theory
• Magic Framework
• Computer Programming
• Dynamic Programming

Regular expression (Regex)

• Fetch the textual information from logs.
• Perform the changes in the existing textual information for re-using.

API Python:

• This section helps you understand the working on API and how to implement the same using Python.
• Here we will learn how to get and post the request using API and implement the same.
• Will create a simple currency conversion calculator.
• We will also cover API for a website which we need to sign in. We will be using the API keys and ID to log in and fetch the details.
• We will explain how to structure and export the data in CSV using Pandas.

Scraping

• Fetch the data from the URL
• Get the information from Robot robot-protected website.
• Fetch the information using pagination
• Fetch the information by crawling the pages and storing it in DB.

Pandas

• Creation of Data Representation
• Data filtering
• Data Framework
• Selection and viewing
• Data Manipulation

Numpy

• Datatypes in Numpy
• Creating arrays and Matrix.
• Manipulation of data.
• Standard deviation and variance.
• Reshaping of Matrix.
• Dot function
• Mini-project using Numpy and Pandas package

Matplotlib

• Creation Plots - Line, Scatter, bar, and Histogram.
• Creating plots from Pandas and Numpy data
• Creation of subplots
• Customization and saving plots

Scikit Learn

End to end Implementation of Data Science and Machine Learning models using Scikit-Learn(SKLearn)

Explained the option of improving the results by changing parameters and Hyper-parameter in a model.

• Getting data ready
• Choosing estimators
• Fitting the data
• Predicting values
• Evaluation of results
• Improving the results of the model
• Saving the model

Supervised Learning

• Data analysis and Basic Plotting
• Data Correlation in modeling
• Getting data ready for modeling
• The model explained in Detail
• Improving the Model Randomized SearchCV
• Grid Search CV

Unsupervised Learning

• K-Means Clustering
• Finding Distance between Clusters
• Hierarchial Clusterng
• Mini-Project

Who this course is for:

• Beginners who are willing to learn to Code or program
• People willing to learn programming from scratch
• Get all python related information in a single course