Hard Pill To Swallow: π
Robots arenβt stealing your future - theyβre taking the boring jobs.
Meanwhile:
- Some YouTuber made six figures sharing what she loves.
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.
Hereβs the thing:
Hard work still matters. But the rules of the game have changed.
The real money is in solving problems, spreading ideas, and building cool stuff.
Call it evolution. Call it disruption. Whatever.
Crying about the old world won't help you thrive in the new one.
Create something.β¨
#ai
Robots arenβt stealing your future - theyβre taking the boring jobs.
Meanwhile:
- Some YouTuber made six figures sharing what she loves.
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.
Hereβs the thing:
Hard work still matters. But the rules of the game have changed.
The real money is in solving problems, spreading ideas, and building cool stuff.
Call it evolution. Call it disruption. Whatever.
Crying about the old world won't help you thrive in the new one.
Create something.β¨
#ai
π21β€13π5π3
AI/ML Roadmapπ¨π»βπ»πΎπ€ -
==== Step 1: Basics ====
π Learn Math (Linear Algebra, Probability).
π€ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
π’ Clean & Visualize Data (Pandas, Matplotlib).
ποΈββοΈ Learn Core Algorithms (Linear Regression, Decision Trees).
π¦ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
π‘ Understand Neural Networks.
πΌοΈ Learn TensorFlow or PyTorch.
π€ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
π³ Study Advanced Algorithms (Random Forest, XGBoost).
π£οΈ Dive into NLP or Computer Vision.
πΉοΈ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
π¨ Create real-world projects.
π Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
==== Step 1: Basics ====
π Learn Math (Linear Algebra, Probability).
π€ Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
π’ Clean & Visualize Data (Pandas, Matplotlib).
ποΈββοΈ Learn Core Algorithms (Linear Regression, Decision Trees).
π¦ Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
π‘ Understand Neural Networks.
πΌοΈ Learn TensorFlow or PyTorch.
π€ Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
π³ Study Advanced Algorithms (Random Forest, XGBoost).
π£οΈ Dive into NLP or Computer Vision.
πΉοΈ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
π¨ Create real-world projects.
π Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
π15β€5π₯1