Neuronex machine learning courses

Find Your Machine Learning Path

Each course is designed to meet learners at their current level and guide them toward practical capabilities in machine learning.

Return Home

Courses for Every Stage of Your Journey

Whether you're beginning to explore machine learning or ready to apply advanced techniques, we offer courses that provide clear instruction and practical experience tailored to your needs.

All courses share our core methodology of patient teaching, hands-on projects, and personalized support. The difference lies in the technical depth and prerequisites for each program.

Machine Learning Foundations course
Beginner Friendly

Machine Learning Foundations

This program introduces learners to machine learning concepts with patient, clear instruction. Participants explore supervised and unsupervised learning paradigms through intuitive explanations. The curriculum covers linear regression, classification algorithms, and model evaluation metrics. Learners implement algorithms using Python and scikit-learn in guided exercises. The program emphasizes understanding over memorization, building genuine comprehension. Mathematical concepts are introduced gradually with practical context.

Supervised and unsupervised learning concepts
Python implementation with scikit-learn
Gradual introduction to mathematical foundations
Model evaluation and performance metrics

Deep Learning Essentials

This course guides learners through neural network architectures and deep learning frameworks. Participants explore perceptrons, activation functions, and backpropagation with supportive instruction. The curriculum covers CNNs for image processing and RNNs for sequential data. Learners build models using TensorFlow and Keras with step-by-step guidance. The program includes practical projects applying deep learning to real problems. GPU computing concepts and cloud training options receive attention.

Neural network architectures and training
CNNs for image processing applications
RNNs for sequential data analysis
TensorFlow and Keras implementation
Deep Learning Essentials course
Intermediate Level
Applied ML Workshop course
Advanced Application

Applied ML Workshop

This workshop focuses on deploying machine learning models in practical applications. Participants learn feature engineering, model optimization, and deployment strategies. The curriculum covers MLOps basics, model monitoring, and continuous improvement practices. Learners work on end-to-end projects from data preparation to production deployment. The program addresses ethical AI considerations and bias detection. Collaborative projects simulate real-world ML engineering scenarios.

Feature engineering and model optimization
MLOps and deployment strategies
Model monitoring and continuous improvement
Ethical AI and bias detection practices

Choose Based on Your Experience Level

Aspect ML Foundations Deep Learning Applied ML
Prerequisites Basic programming knowledge ML foundations or equivalent ML and deep learning basics
Focus Area Core ML concepts and algorithms Neural networks and architectures Production deployment and MLOps
Primary Tools Python, scikit-learn TensorFlow, Keras Full ML stack and deployment tools
Best For Beginners starting ML journey Advancing to neural networks Real-world application focus
Investment ₹28,000 ₹38,000 ₹45,000

Which Course Is Right for You?

Start with Foundations

If you're new to machine learning or want to build a solid understanding of core concepts, begin with Machine Learning Foundations. This course provides the groundwork for all future learning.

Progress to Deep Learning

If you have ML basics and want to work with neural networks for image or sequence problems, Deep Learning Essentials builds on foundational knowledge to explore advanced architectures.

Apply in Production

If you understand ML and deep learning but want to deploy models in real applications, Applied ML Workshop teaches the practical skills needed for production ML systems.

Not Sure Which Course to Choose?

We're happy to discuss your background and goals to help you select the right starting point. Many students progress through multiple courses as their skills develop.

Recommended Learning Progression

Stage 1

Machine Learning Foundations

Build core understanding of ML concepts, algorithms, and Python implementation.

Stage 2

Deep Learning Essentials

Advance to neural networks, CNNs, RNNs, and deep learning frameworks.

Stage 3

Applied ML Workshop

Learn deployment, MLOps, and production ML system development.

Ready to Begin Your Machine Learning Journey?

Choose the course that matches your current level and goals. We're here to support you every step of the way.

Discuss Your Course Options