2025 Python Powerhouse: Gen AI from Basics to Advanced

2025 Python Powerhouse Gen AI From Basics to Advanced Programming

Crack the AI Code with Python Python: The Secret Weapon for AI Success Elevate Your AI Game with Python Python: Your Key

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Last Updated: 2025-01-31 08:01:02 English Course Type: Self Learning

Build robust web applications using Python and Flask
Master Flask fundamentals: routing, templates, forms, and sessions.
Design and implement RESTful APIs with Flask
Master Flask fundamentals and advanced concepts
Deploy Flask applications to production environments
Deploy Flask applications to production environments.

Welcome to Master the Machine Muse: Python Programming for Generative Art & Design, a comprehensive course designed to blend the realms of art and technology through the power of Python programming. This course is meticulously crafted for those who wish to harness the capabilities of Python and generative AI to create stunning visual art and innovative design solutions.

In this course, you'll embark on a journey that starts with the fundamentals of Python and progresses through advanced techniques in generative art and design. Whether you're a beginner or an experienced programmer, this course provides a structured pathway to mastering Python’s essential features and applying them to the creative field of generative art.

Section 1: Lists and Tuples in Python

Begin your journey by understanding the foundational data structures in Python—lists and tuples. You'll explore how to create, manipulate, and utilize these structures effectively. Through practical exercises and case studies, you'll learn to perform operations such as sorting, indexing, and slicing lists, as well as understand the immutable nature of tuples. This section provides a solid grounding in Python's core data types, essential for any advanced programming tasks.

Section 2: Sets and Dictionaries in Python

Delve into more advanced data structures: sets and dictionaries. Learn to define and manipulate sets, handle various operations like union, intersection, and difference, and manage dictionaries with key-value pairs. This section equips you with the skills to handle more complex data scenarios and optimize your data management strategies.

Section 3: Date and Time Manipulations

Master the art of handling date and time data with Python. You'll learn how to format and manipulate date-time objects, extract meaningful components, and apply these skills in real-world data scenarios. This section includes a case study on customer churn prediction, where you’ll practice data preprocessing and feature engineering techniques crucial for accurate data analysis.

Section 4: Functional Programming

Explore functional programming paradigms with Python, focusing on lambda functions, map, reduce, and filter operations. Understand how to apply these concepts to simplify code and perform efficient data transformations. This section provides a deep dive into functional programming techniques, offering practical examples and applications.

Section 5: Advanced Python Programming

Expand your Python expertise with advanced programming concepts such as recursion, feature engineering, and data analysis. You'll tackle problems like handshake calculations using iterative and recursive methods, and learn to engineer date-time features for machine learning models. This section also includes an in-depth look at the IRIS dataset, enhancing your understanding of evaluation metrics like precision, recall, and AUC ROC.

Section 6: Python Libraries and Data Analysis

Gain proficiency in using Python’s standard libraries for mathematical operations, random number generation, and file handling. Explore exploratory data analysis techniques through practical examples, such as football play analysis, and understand how to manage and clean data effectively.

Section 7: Advanced Data Visualization Techniques

Learn advanced visualization techniques to interpret and present data insights. This section covers distribution plots, KDE plots, joint plots, and the identification of outliers. You’ll develop skills to create compelling visual representations of data, aiding in both analysis and storytelling.

Section 8: Model Building and Evaluation

Finally, apply your skills to build and evaluate machine learning models. Learn the process of data preprocessing, model training, and evaluation with a focus on logistic regression. Understand key evaluation metrics and how to interpret them to improve your model’s performance.

By the end of this course, you will have a robust understanding of Python programming and its application in generative art and design. You will be equipped with the skills to create innovative art, handle complex data, and apply advanced techniques to real-world problems. Whether you aim to pursue a career in data science, art, or design, this course will provide the foundational and advanced skills needed to excel in these fields.

Basic Python programming knowledge is recommended. No prior Flask experience is required.
Basic Python programming knowledge
Understanding of HTML, CSS, and JavaScript (recommended)

Python developers looking to expand into web development
Aspiring web developers interested in using Python
Individuals wanting to build web applications with a lightweight framework
Anyone curious about creating interactive online experiences
Aspiring web developers seeking a hands-on approach
Students or professionals interested in building web applications

Section 1 : Introduction To Python And Lists And Python Fundamentals Basics for Artists 3 Lectures 00:18:52
Lecture 1 :
Python Lists: Building Blocks for Creative Data Lists and Basic Operations Preview
Lecture 2 :
List Manipulation and Applications Unleashing Creativity with Python Lists
Lecture 3 :
Tuples and Data Analysis for Artists and Data Analysis
Section 2 : Data Structures for Creative Coding Essential Data Structures 4 Lectures 00:16:08
Lecture 1 :
Sets: A Foundation for Unique Art Sets and Set Operations
Lecture 2 :
Organizing Your Art with Dictionaries and Data Organization
Lecture 3 :
Text as Art: String Manipulation in Python Strings and Text Manipulation
Lecture 4 :
Resources
Section 3 : Data Analysis and Visualization Data-Driven Art 3 Lectures 00:24:22
Lecture 1 :
Working with Time: Datetime Manipulation in Python Datetime Manipulation
Lecture 2 :
Unveiling Data Stories Data Exploration and Cleaning
Lecture 3 :
Visualizing Data, Inspiring Art Data Visualization
Section 4 : Building Blocks of Creativity: Functions and Programming Paradigms Functions 4 Lectures 00:16:39
Lecture 1 :
Map, Reduce, and Conquer: Functional Programming Essentials
Lecture 2 :
Building Blocks of Creativity: Functions in Python
Lecture 3 :
Function Mastery: Arguments, Scope, and Beyond
Lecture 4 :
Resources
Section 5 : Recursion and Global Variables 3 Lectures 00:24:07
Lecture 1 :
Recursive Art: Unlocking Patterns with Python
Lecture 2 :
Time as a Feature: Engineering Date-Time Attributes
Lecture 3 :
Unveiling the Iris Dataset: Classification and Evaluation
Section 6 : Standard Libraries and EDA 4 Lectures 00:19:47
Lecture 1 :
Python's Toolkit: Essential Math and Random Libraries
Lecture 2 :
Exploring Your Data: File Handling and EDA
Lecture 3 :
Finding Patterns: Correlation and Visualization
Lecture 4 :
Resources
Section 7 : Distribution and Outlier Detection 3 Lectures 00:17:22
Lecture 1 :
Understanding Your Data's Shape: Distribution Plots
Lecture 2 :
Spotting the Unusual: Outlier Detection Techniques
Lecture 3 :
Mastering Outliers: Advanced Detection and Handling
Section 8 : Data Preprocessing and Model Building 3 Lectures 00:13:52
Lecture 1 :
Preparing Your Data for Art: Preprocessing Techniques
Lecture 2 :
Predictive Patterns: Logistic Regression for Artists
Lecture 3 :
Resources

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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic.
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Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
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Do i need a pc to access the course or can i do it on mobile & tablet as well?
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AKHIL VYDYULA,

2125 Course Views

19 Courses

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.

Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.

I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!