Overview of Machine Learning & Cloud
The synergy between Machine Learning (ML) and Cloud Computing represents a transformative convergence that is reshaping technology and data-driven innovation. Artificial learning, a cornerstone of ML and artificial intelligence, focuses on developing algorithms that enable systems to autonomously learn and improve from data. This fundamental concept underpins the advancement of AI and ML models, allowing them to make predictions, classify data, and optimize decisions based on learned patterns. Learning in machine learning encompasses a spectrum of techniques, including supervised learning, unsupervised learning, and reinforcement learning, which collectively form the backbone of intelligent systems.
Ariyath Academy, a software institution in Tiruvannamalai specializing in machine learning education, plays a crucial role in nurturing the next generation of AI and ML professionals. Through comprehensive courses and hands-on projects, Ariyath Academy equips students with the skills and knowledge necessary to thrive in the dynamic landscape of AI deep learning and machine learning. As AI technologies continue to evolve and integrate with Cloud Computing, they are poised to revolutionize industries, drive innovation, and pave the way for intelligent and scalable computing solutions that shape the future of technology.
Syllabus
- • Overview of machine learning concepts and algorithms
- • Supervised and unsupervised learning
- • Common machine learning applications
- • Introduction to cloud computing models (IaaS, PaaS, SaaS)
- • Cloud service providers (AWS, Azure, Google Cloud)
- • Cloud deployment models (public, private, hybrid)
- • Basics of Python programming
- • Key Python libraries for machine learning (NumPy, Pandas, Scikit-learn)
- • Linear and logistic regression
- • Decision trees and random forests
- • Support Vector Machines (SVM)
- • Neural networks and deep learning
- • Cleaning and transforming datasets
- • Feature scaling and selection
- • Cross-validation techniques
- • Evaluation metrics for machine learning models
- • Hyperparameter tuning
- • Cloud-based ML platforms (AWS SageMaker, Azure Machine Learning, Google AI Platform)
- • Using cloud services for model training and deployment
- • Processing large datasets with cloud-based tools (e.g., Apache Spark)
- • Distributed computing for ML tasks
- • Overview of serverless architecture
- • Deploying machine learning models as serverless functions
- • Embedding ML models in cloud-based applications
- • API integration for real-time predictions
- • Building end-to-end ML workflows
- • Automating model deployment and updates
- • Considerations for data security and privacy
- • Compliance with regulations (e.g., GDPR)
- • Applying ML and Cloud concepts to real-world scenarios
- • Solving business problems through practical projects
- • Hands-on experience with cloud-based ML services
- • Addressing ethical concerns in ML and Cloud
- • Fairness and transparency in AI algorithms
- • Culminating project applying acquired skills
- • Demonstrating end-to-end ML and Cloud solution development
- • Presentation and documentation of the project
Why Cloud computing in Machine Learning
Even though both cloud computing and machine learning are cutting-edge technologies, machine learning is relatively newer. However, when combined, they offer powerful capabilities that drive companies' progress. Machine learning involves creating intelligent software or machines, while cloud computing provides the necessary storage and security to access these applications.
The key link between machine learning and cloud computing lies in resource management. Machine learning tasks demand significant processing power, data storage, and multiple servers working simultaneously on algorithms. Here, cloud computing steps in by offering scalable resources over the internet, allowing users to access new servers with predefined data and adjust resources as needed.
Cloud computing primarily handles computation tasks, which are crucial for machine learning's complex data processing needs. Not everyone has access to numerous powerful machines, making cloud computing a valuable resource for machine learning tasks such as task scheduling and data storage.
Advantages of Machine Learning with Cloud Computing
1. Saves Money: Cloud services let you pay only for what you use. This is great for businesses wanting to use machine learning without spending too much upfront.
2. Easy to Use: You can work with machine learning tools on the cloud even if you're not an expert in data science. The platforms are designed to be user-friendly.
3. Easy Testing and Growth: Cloud platforms make it simple to try out different machine learning tools. As your projects grow and need more resources, the cloud can easily scale up to support them.
There are so many cloud service providers that offer lots of ML technologies for everyone without having prior knowledge of AI and ML.
Upcoming Batches
| November 8 | SAT & SUN weekend batch |
Timings 9:00AM to 11:00AM |
| November 22 | SAT & SUN Online batch |
Timings 9:00AM to 11:00AM |
| November 13 | MON & THU Offline batch |
Timings 9:00AM to 11:00AM |
| November 27 | MON & THU Blended batch |
Timings 9:00AM to 11:00AM |