2025 Master the Machine Muse Build Generative AI with ML

Learn to create and deploy generative AI models using machine learning. Explore frameworks, tools, and practical ml

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Last Updated: 2025-01-30 05:01:38 English Course Type: Self Learning

Implement practical applications of generative AI in various domains.
Build and deploy generative AI models using popular frameworks and tools.
Craft generative models using machine learning techniques
Train AI to generate creative text formats (like poems!)
Master the fundamentals of Generative Adversarial Networks (GANs)
Understand the fundamentals of generative AI and machine learning.

Unlock the creative potential of artificial intelligence with "Master the Machine Muse: Build Generative AI with ML." This comprehensive course takes you on an exciting journey into the world of generative AI, blending the art of machine learning with the science of creativity. Whether you're an aspiring data scientist, a tech enthusiast, or a creative professional looking to harness the power of AI, this course will provide you with the skills and knowledge to build and deploy your generative models.

Course Highlights:

- Introduction to Generative AI: Understand the fundamentals of generative AI and its applications across various domains such as art, music, text, and design.

- Foundations of Machine Learning: Learn the core concepts of machine learning, including supervised and unsupervised learning, and how they apply to generative models.

- Deep Learning for Creativity: Dive deep into neural networks and explore architectures like GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformers that are driving the generative AI revolution.

- Hands-On Projects: Engage in practical, hands-on projects that will guide you through the process of building your generative models. From generating art to composing music, you'll experience the thrill of creating with AI.

- Python Programming: Gain proficiency in Python programming, focusing on libraries and frameworks essential for generative AI, such as TensorFlow, PyTorch, and Keras.

- Ethics and Future of Generative AI: Discuss the ethical considerations and future implications of generative AI, ensuring you are well-equipped to navigate this rapidly evolving field responsibly.

Who Should Enroll:

- Data Scientists and Machine Learning Engineers looking to specialize in generative models.

- Artists, Musicians, and Designers interested in exploring AI as a tool for creativity.

- Tech Enthusiasts and Innovators eager to stay ahead in the field of AI.

- Students and Professionals aiming to enhance their skill set with cutting-edge technology.

Prerequisites:

- Basic understanding of Python programming.

- Familiarity with machine learning concepts is beneficial but not required.

Course Outcomes:

By the end of this course, you will:

- Have a strong grasp of generative AI concepts and techniques.

- Be able to build and train generative models using state-of-the-art machine learning frameworks.

- Understand the ethical considerations and potential impacts of generative AI.

- Be prepared to apply generative AI skills in real-world projects and innovative applications.

Join us in "Master the Machine Muse: Build Generative AI with ML" and embark on a creative journey that merges technology with imagination, empowering you to shape the future of AI-driven creativity.

No prior experience with AI or deep learning is required, but it will be helpful.
A computer with internet access to download necessary software and tools.
Basic understanding of programming concepts (preferably in Python).
Curious coders with a basic understanding of Python
Artists and designers who want to explore AI-powered tools
Anyone fascinated by the potential of generative AI

Basic programming experience (Python preferred, but not required)
Aspiring Data Scientists
Machine Learning Enthusiasts
Software Developers
Tech Professionals
Who want to delve into the world of generative AI
Students and Researchers who wish to explore advanced AI concepts and applications.

Section 1 : Master the Machine Muse Build Generative AI with ML 3 Lectures
Lecture 1 :
Logistic Regression: From Zero to Hero Preview
Lecture 2 :
Demystifying Logistic Regression Math
Lecture 3 :
Logistic Regression: Real-World Examples You Can't Ignore
Section 2 : Data Preparation and Evaluation 4 Lectures 00:28:26
Lecture 1 :
Data Cleaning: The Unsung Hero of ML
Lecture 2 :
Feature Engineering Magic: Transform Your Data
Lecture 3 :
Know Your Model: Essential Evaluation Metrics
Lecture 4 :
Resources
Section 3 : Logistic Regression for NLP 3 Lectures 00:24:56
Lecture 1 :
NLP for Beginners: Start with Logistic Regression
Lecture 2 :
Supercharge Your NLP with Advanced Techniques
Lecture 3 :
Transfer Learning: The NLP Shortcut You Need
Section 4 : Logistic Regression in Action: COVID-19 Case Study 4 Lectures 00:28:08
Lecture 1 :
Taming COVID-19 Data: A Data Scientist's Guide
Lecture 2 :
Unmasking COVID-19 Trends: Data-Driven Insights
Lecture 3 :
The Machine Learning Lifecycle: From Data to Deployment
Lecture 4 :
Resources
Section 5 : Text Preprocessing and EDA 3 Lectures 00:37:39
Lecture 1 :
Text Preprocessing: Clean Up Your Act
Lecture 2 :
Advanced Text Preprocessing: Unlock Hidden Patterns
Lecture 3 :
Telling Stories with Text Data: EDA Mastery
Section 6 : Feature Engineering for NLP 3 Lectures 00:23:45
Lecture 1 :
Feature Engineering: The Secret to NLP Success
Lecture 2 :
Optimize Your Model: Hyperparameter Tuning Tips
Lecture 3 :
Finding the Perfect Hyperparameters: A Practical Guide
Section 7 : Regularization and Model Comparison 3 Lectures 00:25:55
Lecture 1 :
Regularization: Prevent Overfitting Like a Pro
Lecture 2 :
Which Model Wins? A Showdown
Lecture 3 :
Linear Regression: The Building Block of ML
Section 8 : Linear Regression and Decision Trees 3 Lectures 00:22:24
Lecture 1 :
Linear Regression: Simple Models, Big Impact
Lecture 2 :
Boost Your Linear Regression Game
Lecture 3 :
Decision Trees: Easy to Understand, Powerful to Use
Section 9 : Decision Tree Algorithms 3 Lectures 00:22:58
Lecture 1 :
Decision Trees: The Building Blocks
Lecture 2 :
Mastering Entropy and Information Gain
Lecture 3 :
Avoid Overfitting: Deep Dive into Decision Trees
Section 10 : Decision Trees with Categorical Data 3 Lectures 00:26:08
Lecture 1 :
Handling Categorical Data: Decision Tree Style
Lecture 2 :
Train and Conquer: Decision Tree Mastery
Lecture 3 :
Data-Driven Insights: Univariate Analysis
Section 11 : Data Visualization and Analysis 3 Lectures 00:39:36
Lecture 1 :
Data Visualization: Tell Your Story Visually
Lecture 2 :
Spotting Trends: Outliers and Correlations
Lecture 3 :
Advanced Visualization: Uncover Hidden Insights
Section 12 : Advanced Data Analysis 4 Lectures 00:30:17
Lecture 1 :
Bivariate Analysis: Uncover Relationships
Lecture 2 :
Multivariate Analysis: Mastering Complexity
Lecture 3 :
Time Series Analysis: Forecasting the Future
Lecture 4 :
Resources
Section 13 : Clustering Techniques 3 Lectures 00:30:18
Lecture 1 :
K-means Clustering: Find Your People
Lecture 2 :
Mastering K-means: Tips and Tricks
Lecture 3 :
K-means in Action: Real-World Examples
Section 14 : Advanced Clustering and Evaluation 3 Lectures 00:15:20
Lecture 1 :
Beyond K-means: Advanced Clustering Techniques
Lecture 2 :
Evaluating Your Clusters: Does It Make Sense?
Lecture 3 :
Resources

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AKHIL VYDYULA,

1568 Course Views

16 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!