Daniel

Honestly, you don’t need fancy bootcamps to get started. Just know your basic calculus and matrix algebra, start learning ML, then move on to MLPs once the basics click.
              

Yuki

Yup, focus on understanding the intuition behind things, not just memorizing formulas. ML only makes sense when you “feel” the math.
              

Laura

Couldn’t agree more. Pick one solid book for structure, then Google stuff when you get stuck. That’s literally how most of us learned.
              

Omar

Bootcamps and Datacamp are fine if you’re dabbling, but they barely scratch the surface. If you’re serious, skip the paywalls.
              

Daniel

Here’s the roadmap that worked for me:
 1️⃣ Learn Python first: syntax, loops, functions.
 2️⃣ Study math: multivariate calculus, linear algebra, stats, probability.
 3️⃣ Learn NumPy: it’s essential.
 4️⃣ Read about basic ML algorithms (clustering → decision trees → random forest).
 5️⃣ Try coding them from scratch.
              

Léa

For neural networks, start with Andrej Karpathy’s intro video. It’s seriously the best 1-hour breakdown of how NNs work.
              

Ahmed

And don’t sleep on Stanford’s CS231n or the 3Blue1Brown series, both are gold. Also, the deeplearning.ai and fast.ai courses are great for practical ML.
              

Anastasia

Exactly. Build, break, repeat. Coding those algorithms by hand teaches more than any theory ever will.
              

Pablo

People underestimate how critical statistics is. Like, it’s the foundation of ML.
              

Tomasz

So true. Everyone just dives straight into TensorFlow tutorials without even knowing what a p-value is.
              

Lena

Learn stats properly, and maybe even play around with R before jumping to Python ML libs. It builds great habits.