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Verified Program
Machine Learning
Build and train ML models, work with datasets, and solve prediction problems.
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Plan Summary
Duration
30 Days Plan
Commitment
3-4 hours / day
Focus
Advanced ensemble methods, unsupervised clustering, neural network basics, and production model saving.
Training Topics
- Ensemble machine learning (Random Forests, Gradient Boosting, XGBoost)
- Unsupervised clustering models (K-Means, PCA dimensional compression)
- Saving and deploying ML pipelines using Joblib or Flask web wrappers
- Introduction to neural networks using TensorFlow or PyTorch
Training Roadmap & Phases
Week 1
Ensemble Models
Combine predictions using Random Forests and tune XGBoost gradient trees.
Week 2
Clustering & Compression
Group customer files using K-Means and simplify parameters using Principal Component Analysis.
Week 3
Deep Learning Foundations
Build simple multi-layer perceptron networks and track training loss variables.
Week 4
Pipeline Export & API Deployment
Serialize data processors and models, construct a REST API for inferences, and deploy to the cloud.
Ready to Kickstart Your Journey?
Secure your spot in our verified Machine Learning internship. Limited batch sizes for personalized mentorship.