$3,000.00 USD

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Deep Learning - Level 2

Deep Learning Course — Career Outcome: ML Engineer, CV Engineer, NLP Engineer

Master Deep Learning in 3 months through hands-on projects that teach you how to build, train, and apply neural networks in the real world. Learn how machines see images, detect objects, and understand human language — all through practical experience.

What you’ll learn:

  • 🧠 Month 1: Neural Networks & Deep Learning Foundations – Build your own neural networks with PyTorch and apply them to real manufacturing problems.
    Projects: Fashion-MNIST Classifier (basic and CNN upgrade), PCB Defect Classification.

  • 👁️ Month 2: Computer Vision Mastery – Train object detection and image segmentation models.
    Projects: PCB Defect Detection with YOLO, Medical Image Segmentation with U-Net.

  • 💬 Month 3: Understanding Human Language – Dive into Natural Language Processing with Transformers.
    Project: Comment Toxicity Detector for online moderation.

Skills: Neural Networks, PyTorch, CNNs, YOLO, U-Net, NLP, Transformers, Hugging Face.
Guarantee: 100% money-back within the first 2 weeks if you’re not satisfied.

📚 Course Overview

Duration: 3 months

Live Webinars: Twice a week (1–1.5 hours)

Interview Prep & Mentorship: Weekly support starting Month 2

Job Hunt Support: Ongoing until hired

✅ What’s Included

• Step-by-step Online Video Curriculum

• Live Instruction & Q&A Webinars

• Build and deploy your own AI models 

Capstone Project to showcase to employers

24/7 Expert Support & Feedback

• Private Group Chat with Mentors

• Resume & LinkedIn Optimization

• Weekly Mock Interviews

• Lifetime Access to Alumni Community

• Access to course materials for months after graduation

Certificate of Graduation

What People Are Saying:

I came with zero experience in ML- now I can build real models. The structure, support, and feedback were priceless.

Sofia P.

This course finally made Machine Learning make sense. The mentors explain everything step-by-step and push you to think like an engineer.

Daniel R.

Exactly what I needed to break into ML. Clear lessons, real-world tasks, and support anytime I felt stuck.

Mark