All roadmaps
ML/AI
Machine Learning & AI
From Python basics to neural networks, NLP, and real-world ML project deployment.
Duration
20 weeks
Difficulty
Hard
Goal
ML Engineer Role
Modules
12 • 59 topics
What you'll achieve
Train & deploy real ML models
Build 3 end-to-end AI projects
Crack ML/Data roles
Full Syllabus
Every module and topic in this roadmap.
1
Python for ML
NumPy & ArraysPandas DataFramesMatplotlib & SeabornData Cleaning
2
Mathematics Foundation
Linear AlgebraProbability & StatisticsCalculusBayes' Theorem
3
Exploratory Data Analysis
VisualizationFeature EngineeringMissing DataOutliersCorrelation
4
Supervised Learning
Linear/Logistic RegressionDecision Trees & RFSVMKNNNaive BayesXGBoost/LightGBM
5
Unsupervised Learning
K-MeansHierarchicalPCADBSCANAssociation Rules
6
Model Evaluation
Cross-ValidationConfusion Matrix, Precision, Recall, F1ROC-AUCBias-VarianceHyperparameter Tuning
7
Neural Networks
Perceptrons & ActivationsForward/BackpropLoss & OptimizersTensorFlow/KerasPyTorch
8
Deep Learning
CNNsRNNs & LSTMsTransfer LearningGANsAutoencoders
9
NLP
Tokenization/StemmingBoW & TF-IDFWord2Vec/GloVeTransformers & BERTSentiment Analysis
10
Computer Vision
Image PreprocessingYOLO basicsSegmentationOpenCVFace Detection
11
MLOps & Deployment
SerializationFlask/FastAPIDocker for MLMLflowCloud Deployment
12
Capstone Projects
House Price PredictionMovie RecommenderNLP ChatbotImage ClassifierEnd-to-End Pipeline
Prerequisites
Python, basic math
Related resources
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