It's impossible to become AI/ML in 3 month (20-30 LPA)

SHUT THE FUCK UP Follow AI/ML Roadmap with Free Resources Follow & Comment: "3" to get a direct link 3-Month AI/ML Learning Roadmap by SYNTAX ERROR Month 1: Mathematical Foundations & Python Programming Week 1: Python Basics & Environment Setup Monday-Tuesday: Python fundamentals Resource: Python.org Official Tutorial Topics: Variables, data types, control flow Wednesday-Thursday: Python advanced concepts Resource: Real Python Topics: Functions, classes, modules Friday/Weekend: Practice projects Resource: HackerRank Python Build: Simple calculator, to-do list app Week 2: Mathematics for AI/ML Monday-Tuesday: Linear Algebra Resource: Khan Academy Linear Algebra Topics: Vectors, matrices, eigenvalues Wednesday-Thursday: Calculus Resource: Paul's Online Math Notes Topics: Derivatives, partial derivatives, chain rule Friday/Weekend: Statistics & Probability Resource: StatQuest YouTube Channel Topics: Mean, variance, distributions, Bayes theorem Week 3: Data Science Libraries Monday-Tuesday: NumPy Resource: NumPy Official Tutorial Practice: Array operations, broadcasting Wednesday-Thursday: Pandas Resource: Pandas Documentation Practice: Data manipulation, cleaning Friday/Weekend: Matplotlib & Seaborn Resource: Matplotlib Tutorials Project: Data visualization dashboard Week 4: Introduction to ML Monday-Tuesday: ML Fundamentals Resource: Google's Machine Learning Crash Course Topics: Supervised vs unsupervised learning Wednesday-Thursday: Scikit-learn basics Resource: Scikit-learn Clustering Guide Practice: Simple classification problems Friday/Weekend: First ML Project Resource: Kaggle Tutorials Build: Iris dataset classification Month 2: Core Machine Learning Algorithms Week 5: Supervised Learning - Regression Monday-Tuesday: Linear Regression Resource: Andrew Ng's Course (Coursera) Implementation: Approach Ng's course with self-learn Wednesday-Thursday: Polynomial & Ridge Regression Resource: Towards Data Science Topics: Polynomial regression, pricing prediction Friday/Weekend: Logistic Regression Resource: StatQuest Logistic Regression Project: Credit card fraud detection Week 6: Supervised Learning - Classification Monday-Tuesday: Decision Trees Resource: Visual Introduction to ML Topics: Supervised vs unsupervised learning Wednesday-Thursday: Support Vector Machines (SVM) Resource: SVM.Explorers.io Project: Image classification Week 7: Unsupervised Learning Monday-Tuesday: K-Means Clustering Resource: K-Means Clustering Visualization Topics: Customer segmentation Wednesday-Thursday: Hierarchical Clustering & DBSCAN Resource: Scikit-learn Clustering Guide Implementation: Document clustering Friday/Weekend: Principal Component Analysis (PCA) Resource: PCA Visualization Project: Dimensionality reduction visualization Week 8: Model Evaluation & Improvement Monday-Tuesday: Cross-validation & Metrics Resource: Machine Learning Mastery - Metrics Topics: Accuracy, precision, recall, F1-score Wednesday-Thursday: Feature Engineering Resource: Python Data Science Handbook Topics: Creating polynomial features Friday/Weekend: Hyperparameter Tuning Resource: Kaggle Feature Engineering Course Project: End-to-end ML pipeline Month 3: Deep Learning & Advanced Topics Week 9: Neural Networks Fundamentals Monday-Tuesday: Perceptron & Activation Functions Resource: Neural Networks and Deep Learning Implementation: Build perceptron from scratch Wednesday-Thursday: Backpropagation Resource: Blue.Brown Neural Network Series Practice: Gradient descent visualization Friday/Weekend: Introduction to TensorFlow/Keras Resource: TensorFlow Tutorials Project: MNIST digit classifier Week 10: Convolutional Neural Networks (CNN) Monday-Tuesday: CNN Architecture Resource: Stanford CS231n Topics: Convolutional pooling, filters Wednesday-Thursday: Popular CNN Architectures Resource: Papers with Code Study: LeNet, AlexNet, VGG Friday/Weekend: CNN Project Resource: Keras CNN Examples Build: Image classification app Week 11: Recurrent Neural Networks (RNN) Monday-Tuesday: RNN & LSTM Basics Resource: Understanding LSTM Networks Topics: Sequence modeling, vanishing gradients Wednesday-Thursday: Transformer Networks Resource: Deep Learning Book Topics: Attention mechanism, positional encoding Friday/Weekend: Advanced RNNs Resource: PyTorch Tutorials Project: Text generation