*Roadmap for Learning Machine Learning (ML)* Here’s a concise and point-wise roadmap for learning ML: 1. Prerequisites - Learn programming basics (e.g., Python). - Understand mathematics: 1 - Linear Algebra (vectors, matrices). 2 - Probability and Statistics (distributions, Bayes’ theorem). 3 - Calculus (derivatives, gradients). 4 - Familiarize yourself with data structures and algorithms. 2. Basics of Machine Learning -Understand ML concepts: Supervised, unsupervised, and reinforcement learning. Training, validation, and testing datasets. - Learn how to preprocess and clean data. - Get familiar with Python libraries: NumPy, Pandas, Matplotlib, and Seaborn. 3. Supervised Learning - Study regression techniques: Linear and Logistic Regression. - Explore classification algorithms: Decision Trees, Support Vector Machines (SVM), k-NN. - Learn model evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC. 4. Unsupervised Learning - Learn clustering techniques: k-Means, DBSCAN, Hierarchical Clustering. - Understand Dimensionality Reduction: PCA, t-SNE. 5. Advanced Concepts - Explore ensemble methods: Random Forest, Gradient Boosting, XGBoost, LightGBM. - Learn hyperparameter tuning techniques: Grid Search, Random Search. 6. Deep Learning (Optional for Advanced ML) - Learn neural networks basics: Forward and Backpropagation. - Study Deep Learning libraries: TensorFlow, PyTorch, Keras. Explore CNNs, RNNs, and Transformers. 7. Hands-on Practice - Work on small projects like: 1 - Predicting house prices. 2 - Sentiment analysis on tweets. 3 - Image classification. 4 - Explore Kaggle competitions and datasets. 8. Deployment - Learn how to deploy ML models: Use Flask, FastAPI, or Django. - Explore cloud platforms: AWS, Azure, Google Cloud. 9. Keep Learning - Stay updated with new techniques: Follow blogs, papers, and conferences (e.g., NeurIPS, ICML). - Dive into specialized fields: NLP, Computer Vision, Reinforcement Learning. Join for more: https://t.me/datalemur