Machine
Learning
Machine Learning Mastery: Smarter Decisions with Data
Who this course is for
What you'll learn
- Explore the basics of Machine Learning, the leading technology for data-driven intelligence and automation.
- Understand the core concepts behind machine learning, including algorithms, data processing, and model training
- Acquire practical skills by working with core machine learning tools and techniques.
- Progress from beginner to advanced machine learning topics, building a strong foundation and deep expertise.
Learning Journey
The Course Includes
- Classroom Training
- 100+ Exercises
- Study Materials
- Certification
- 1 to 1 Mentorship
- Life-Time Community Membership
- Hands-On Project
- Interactive Webinars
- Career Support
Course Highlight
Top Faculty
Learn from experienced cloud professionals dedicated to high-quality education.
Full Curriculum
Our courses cover all essential cloud computing topics in a comprehensive manner.
Career Support
Get personalized support, including resume building and interview prep.
Hands-On Learning
Engage in interactive sessions offering practical insights and hands-on experience.
Course Content
Introduction
Code & Slides Download
What is Machine Learning?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Data Preprocessing & Feature Engineering
Model Evaluation & Validation
Neural Networks & Deep Learning
TensorFlow & Keras Basics
Model Deployment & Scaling
Working with Large Datasets
Cloud Integrations for Machine Learning
Model Monitoring & Performance Tuning
Market Research in ML
Natural Language Processing (NLP)
Computer Vision
AI Ethics & Fairness
Description
Our Machine Learning course is designed to provide comprehensive training for individuals eager to excel in the fast-growing field of AI and data-driven technologies. Throughout this course, you’ll gain essential knowledge and hands-on experience with key machine learning algorithms, data preprocessing, and model development techniques. You’ll learn how to build, train, and deploy machine learning models, optimize model performance, and understand the ethical implications of AI. Whether you’re just starting your journey in machine learning or looking to advance your skills, this course equips you with the practical expertise and insights needed to thrive in the world of intelligent systems and data science.
Projects
Develop a platform that leverages cloud services to collect, process, and analyze large datasets in real-time. The project can include integration with popular data visualization tools and machine learning algorithms to provide actionable insights.
Create a machine learning-driven web application using cloud services like AWS SageMaker or Google AI Platform. The application can include features like user input processing, predictive analytics, and model training, demonstrating the power of machine learning for real-time decision-making. Showcase the integration of data storage, model deployment, and continuous model updates, highlighting the benefits of cloud-based ML platforms in terms of scalability, automation, and cost-efficiency.
Build a complete machine learning pipeline on a cloud platform, incorporating data collection, preprocessing, model training, testing, and deployment. Use tools like AWS SageMaker, Google AI Platform, or Azure Machine Learning to automate the end-to-end ML workflow. Implement continuous integration (CI) for model training, automated testing for model accuracy, and continuous delivery (CD) for deploying updated models. This project will demonstrate how cloud services can streamline the process of developing, deploying, and managing machine learning models at scale.
Develop a machine learning platform that enables seamless deployment, monitoring, and management of models across multiple cloud providers such as AWS, Azure, and Google Cloud. The platform can include features for model versioning, resource allocation, automated retraining, and model evaluation. It should also offer cost optimization strategies by selecting the best-performing cloud resources, as well as failover mechanisms to ensure high availability and reliability for continuous AI-driven decision-making across different cloud environments.
Skills Covered
Requirements
- This course is designed for individuals who are new to the world of machine learning and AI.
Absolutely no prior experience necessary. - Familiarity with programming, especially Python, will be beneficial but not mandatory.
Tools Required
