🤖 Want to become a Machine Learning Engineer? This free roadmap will get you there! 🚀

📚 Math & Statistics
⦁ Probability 🎲
⦁ Inferential statistics 📊
⦁ Regression analysis 📈
⦁ A/B testing 🔍
⦁ Bayesian stats 🔢
⦁ Calculus & Linear algebra 🧮🔠

🐍 Python
⦁ Variables & data types ✏️
⦁ Control flow 🔄
⦁ Functions & modules 🔧
⦁ Error handling ❌
⦁ Data structures 🗂️
⦁ OOP basics 🧱
⦁ APIs 🌐
⦁ Algorithms & data structures 🧠

🧪 ML Prerequisites
⦁ EDA with NumPy & Pandas 🔍
⦁ Data visualization 📉
⦁ Feature engineering 🛠️
⦁ Encoding types 🔐

⚙️ Machine Learning Fundamentals
⦁ Supervised: Linear Regression, KNN, Decision Trees 📊
⦁ Unsupervised: K-Means, PCA, Hierarchical Clustering 🧠
⦁ Reinforcement: Q-Learning, DQN 🕹️
⦁ Solve regression 📈 & classification 🧩 problems

🧠 Neural Networks
⦁ Feedforward networks 🔄
⦁ CNNs for images 🖼️
⦁ RNNs for sequences 📚 
  Use TensorFlow, Keras & PyTorch

🕸️ Deep Learning
⦁ CNNs, RNNs, LSTMs for advanced tasks

🚀 ML Project Deployment
⦁ Version control 🗃️
⦁ CI/CD & automated testing 🔄🚚
⦁ Monitoring & logging 🖥️
⦁ Experiment tracking 🧪
⦁ Feature stores & pipelines 🗂️🛠️
⦁ Infrastructure as Code 🏗️
⦁ Model serving & APIs 🌐

💡 React ❤️ for more!