🤖
AI
AI & Machine Learning
Supervised learning, unsupervised learning, neural networks, NLP and applied AI projects.
Duration
12 weeks
Students
390+ enrolled
Rating
4.9
What You Will Learn
Understand ML workflows
Build AI models
Work with NLP use cases
Deploy AI-powered applications
Curriculum
1
AI/ML foundations
AI vs ML vs DLUse casesModel lifecycleTools
Lab: Set up Python ML environment
2
Python and math essentials
Linear algebra basicsProbabilityFunctionsData structures
Lab: Implement simple vector operations
3
Data preparation
CleaningEncodingScalingTrain/test split
Lab: Prepare dataset for ML training
4
Supervised learning
RegressionClassificationLoss functionsMetrics
Lab: Build house price and churn models
5
Unsupervised learning
ClusteringDimensionality reductionSegmentationUse cases
Lab: Create customer segmentation model
6
Model tuning
Cross validationHyperparametersGrid searchBias variance
Lab: Tune and compare ML models
7
Neural network basics
PerceptronActivation functionsBackpropagationEpochs
Lab: Train a simple neural network
8
Deep learning concepts
CNN basicsEmbeddingsTransfer learningEvaluation
Lab: Image classification mini project
9
NLP fundamentals
Text cleaningTokenizationVectorizationSentiment analysis
Lab: Build sentiment analysis model
10
Applied AI workflows
Prompting basicsAI app designModel APIsResponsible AI
Lab: Create AI assistant style demo
11
Deployment
Flask/FastAPIModel servingMonitoring basicsCloud options
Lab: Expose trained model through API
12
Capstone and interviews
Project storyEvaluation explanationResume framingMock interview
Lab: Present AI/ML capstone project