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Machine Learning Training in Bangalore — Scikit-learn, Deep Learning, NLP, Computer Vision & MLOps
Master machine learning from linear regression to deep neural networks — supervised & unsupervised algorithms, NLP, computer vision with TensorFlow & PyTorch, model deployment and full MLOps pipelines on AWS SageMaker.
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Skills You Will Master
Hands-on skills taught by working professionals with real MNC project experience.
Course Curriculum
6 structured modules with theory, hands-on labs and live projects.
01 ML Foundations · 5 topics
- Supervised Learning Algorithms
- Unsupervised Learning (Clustering, PCA)
- Model Evaluation & Metrics
- Bias-Variance Tradeoff
- Scikit-learn Pipelines
02 Advanced ML Algorithms · 5 topics
- Ensemble Methods (Random Forest, XGBoost)
- Support Vector Machines (SVM)
- Gradient Boosting (LightGBM, CatBoost)
- Hyperparameter Tuning (Optuna, GridSearch)
- Anomaly Detection
03 Deep Learning · 5 topics
- Neural Network Architecture & Backpropagation
- CNNs for Image Classification
- RNNs & LSTMs for Sequences
- Transfer Learning (ResNet, VGG, BERT)
- TensorFlow & PyTorch Hands-on
04 NLP & Computer Vision · 5 topics
- Text Preprocessing & Vectorization
- Sentiment Analysis & Text Classification
- Named Entity Recognition (NER)
- YOLO Object Detection
- Image Segmentation (U-Net)
05 MLOps & Deployment · 5 topics
- Model Serialization (Pickle, ONNX)
- FastAPI for Model Serving
- Docker Containerisation
- AWS SageMaker & MLflow
- CI/CD for ML Models (GitHub Actions)
06 Projects & Interview Prep · 5 topics
- Fraud Detection System
- NLP Sentiment Dashboard
- Image Classification API
- Kaggle Competition Strategy
- ML System Design Interviews
Tools & Technologies
Career Roles You Can Target
Machine Learning Engineer
Deep Learning Engineer
NLP Engineer
Computer Vision Engineer
MLOps Engineer
AI Research Engineer
Industry Certifications
- TensorFlow Developer Certificate
- AWS Certified Machine Learning – Specialty
- Microsoft Azure AI Engineer Associate (AI-102)
- Google Professional ML Engineer
Frequently Asked Questions
What is the difference between AI and Machine Learning?
AI is the broad concept of machines performing intelligent tasks. Machine Learning is a subset of AI where machines learn patterns from data. Deep Learning is a subset of ML using neural networks.
What prerequisites do I need for ML training?
Python programming and basic statistics are required. Our Data Science course is an ideal prerequisite — or you can join this course with relevant experience.
What salary does an ML engineer earn?
Junior ML engineers earn ₹10–15 LPA in Bangalore. Mid-level earn ₹18–35 LPA. Senior ML engineers and AI architects earn ₹40–80 LPA at top MNCs and startups.
Will I learn TensorFlow or PyTorch?
Both — TensorFlow and Keras for production deployment, PyTorch for research and cutting-edge model development. Both are industry standard.
Does the course cover MLOps?
Yes — model deployment with FastAPI, Docker containerisation, AWS SageMaker, MLflow experiment tracking and CI/CD pipelines for ML are all covered.
Get Course Details
Our counsellor will contact you within 30 mins with syllabus, batch dates & fees.
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