DATA SCIENCE + ARTIFICIAL INTELLIGENCE (AI)

• Python programming fundamentals
  
• Data structures & functions
  
• Working with NumPy arrays
  
• Data manipulation using Pandas
  
• Data visualization basics
  Outcome: Proficiency in Python for analytics

• Data cleaning & preprocessing
  
• Handling missing values & outliers
  
• Feature understanding & transformation
  
• Data visualization & insights generation
  
• Statistical analysis for decision-making
  Outcome: Ability to derive insights from raw data

• Descriptive & inferential statistics
  
• Probability distributions
  
• Hypothesis testing
  
• Correlation & regression concepts
  
• Statistical modeling
  Outcome: Strong statistical foundation for ML models

• Supervised vs Unsupervised learning
  
• Regression & classification algorithms
  
• Decision Trees, Random Forest, XGBoost
  
• Clustering techniques
  
• Model evaluation & validation
  Outcome: Practical machine learning expertise

• Feature engineering
  
• Hyperparameter tuning
  
• Ensemble learning
  
• Model optimization techniques
  
• ML pipelines & automation
  Outcome: Industry-grade ML model development

• Neural network fundamentals
  
• Artificial Neural Networks (ANN)
  
• Convolutional Neural Networks (CNN)
  
• Recurrent Neural Networks (RNN, LSTM)
  
• Model training & optimization
  Outcome: Deep learning and neural network skills

• Text preprocessing & tokenization
  
• Sentiment analysis
  
• Text classification
  
• Named Entity Recognition (NER)
  
• Transformer & LLM fundamentals
  Outcome: NLP and language intelligence expertise

• Image processing fundamentals
  
• Object detection & image classification
  
• OpenCV applications
  
• Generative AI concepts
  
• Prompt engineering & AI use cases
  Outcome: Vision-based AI & Generative AI understanding

• Model deployment concepts
  
• APIs for ML models
  
• Cloud deployment overview
  
• Model monitoring & performance tracking
  
• AI lifecycle management
  Outcome: Production-readiness for AI models

Project Scope:

• Business problem understanding
  
• Data collection & preprocessing
  
• ML / AI model development
  
• Model evaluation & deployment
  
• Final presentation