Machine learning courses
About Machine learning
Machine learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.
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Machine learning courses
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- Prepare for the AIF-C01 AWS Certified AI Practitioner certification exam covering machine learning basics, AI services, data preparation and model deployment.
- Includes 12 Courses
- Beginner Friendly.7 hours
- Prepare for AWS MLS-C01 Machine Learning certification covering model building, training, tuning, deployment, and cloud-based ML implementation.
- Includes 18 Courses
- Intermediate.17 hours
- Study for DP-100 Designing and Implementing a Data Science Solution on Azure certification exam covering machine learning models and data analytics.
- Includes 20 Courses
- Intermediate.22 hours
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- Portfolio projectAI • Python • Machine learning • Data science
Classifying Banking Intent from Customer Queries
This project demonstrates an end-to-end AI engineering project for an NLP task that involves building a classification system predicting banking intent from customer queries by comparing traditional neural networks with modern transformer-based models finetuned with LoRA.Independent,Portfolio Ready - Portfolio projectAI • Python • Data science • Machine learning
Build an AI Agent for Travel Planning
Create an AI-powered trip planning application using Streamlit that demonstrates your skills in building agentic AI systems with tool calling, real-time data integration, and user feedback loops.Independent,Portfolio Ready - Practice projectAI • Machine learning • Python • Data science
Classifying CIFAR-10 with Pretrained CLIP: Original vs Reconstructed Images
Explore how image reconstruction quality affects zero-shot classification performance of a pretrained CLIP model on CIFAR-10.More guidance, - Practice projectData science • Python • Machine learning
Analyzing Recommendation System Performance Across Model Versions
Analyze the performance and cost-effectiveness of an e-commerce recommendation system across three model versions. You’ll investigate whether newer models deliver better results, identify performance differences across customer segments, and determine which model version provides the best ROI.More guidance,
Related articles
- Article
Training Set vs Validation Set vs Test Set
Discover training set vs. validation set vs. test set differences in ML. Learn data splitting best practices for better model performance. - Article
Normalization: Min-Max and Z-Score Normalization
Learn how to normalize data in machine learning using techniques such as min-max normalization and z-score normalization. - Article
Scikit-Learn Tutorial: Python Machine Learning Model Building
Learn how to build powerful machine learning models with scikit-learn in Python. Master essential techniques from installation to implementation with practical examples and comparisons. - Article
What is One Hot Encoding and How to Implement it in Python?
Learn how one-hot encoding works and how to implement it with Pandas and Scikit-learn modules in Python.