What You Should Know Before Starting a Deep Learning Course Online

Thinking about signing up for a deep learning course online? Smart move - but don’t rush into it blindly.

Deep learning is exciting, but it’s also complex. If you're not prepared for what you're walking into, it’s easy to burn out or lose momentum. This blog breaks down the key questions most people have before they start - so you can learn smarter, not harder.

What should you already know before learning deep learning?

You don’t need to be a genius or a PhD student - but you do need some fundamentals.

Before starting a deep learning course, make sure you’re familiar with:

• Python basics (functions, loops, NumPy)
  
  
• Linear algebra and calculus at a high-school level
  
  
• Basic machine learning concepts like supervised vs unsupervised learning
  
  

If these feel unfamiliar, you may want to brush up or take a quick prep course first.

Do you need a strong math background to learn deep learning?

You’ll definitely use math - but you don’t need to panic.

Most deep learning online courses will teach you the math as you go, especially if they’re beginner-friendly. What matters more is being comfortable with:

• Matrices and vectors
  
  
• Derivatives and gradients
  
  
• Probability and statistics
  
  

If the course is good, it’ll explain these concepts visually and practically, not just with equations.

How much time should you set aside each week for an online deep learning course?

It depends on the course, but a good rule of thumb is:

• 6 to 10 hours per week for part-time learning
  
  
• 3 to 5 months to complete a project-based course
  
  

Be realistic. It’s better to go slow and retain than rush through videos and forget everything.

What kind of projects should a good deep learning course include?

A course without projects is just theory. Make sure the program includes hands-on work like:

• Image classification (e.g., using CNNs on datasets like CIFAR or MNIST)
  
  
• Text generation or translation with RNNs or transformers
  
  
• Real-world case studies like fraud detection or object detection
  
  

The best online deep learning courses walk you through projects you can actually add to your portfolio.

What tools and frameworks will you use in most deep learning courses?

You’ll likely work with:

• TensorFlow or PyTorch (industry-standard frameworks)
  
  
• Jupyter Notebooks for coding and visualizations
  
  
• Google Colab or cloud GPUs for training models
  
  

The course should guide you through setup - if it doesn’t, it’s probably not for beginners.

How can you tell if a deep learning course is actually worth it?

Look for these things:

• A clear learning path with structured modules
  
  
• Real projects, not just toy examples
  
  
• Reviews from learners with similar backgrounds
  
  
• Access to mentors or community support
  
  

Want a shortcut? Explore some of the Best Online Courses for Deep Learning with hands-on projects and real outcomes.

Final thought

The secret to success with deep learning courses online isn’t being perfect - it’s being prepared.

Know what you need, choose a course that fits your pace, and focus on learning by doing. The rest will follow.

‍