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Course Outline

Introduction to AI in Python

  • Essential concepts and the scope of AI
  • Python libraries dedicated to AI development
  • Structuring AI projects and defining workflows

Preparing Data for AI

  • Data cleaning, transformation, and feature engineering
  • Managing missing and imbalanced data
  • Scaling features and applying encoding techniques

Supervised Learning Methods

  • Regression and classification algorithms
  • Ensemble techniques: Random Forest and Gradient Boosting
  • Hyperparameter tuning and cross-validation strategies

Unsupervised Learning Methods

  • Clustering approaches: K-Means, DBSCAN, and hierarchical clustering
  • Dimensionality reduction using PCA and t-SNE
  • Practical applications of unsupervised learning

Neural Networks and Deep Learning

  • Getting started with TensorFlow and Keras
  • Constructing and training feedforward neural networks
  • Enhancing the performance of neural networks

Introduction to Reinforcement Learning

  • Key concepts: agents, environments, and rewards
  • Implementing foundational reinforcement learning algorithms
  • Real-world use cases for reinforcement learning

Deployment of AI Models

  • Persisting and retrieving trained models
  • Embedding models into applications through APIs
  • Monitoring and maintaining AI systems in production environments

Recap and Future Directions

Requirements

  • A strong grasp of Python programming fundamentals
  • Proficiency with data analysis libraries such as NumPy and pandas
  • Foundational understanding of machine learning concepts and algorithms

Target Audience

  • Software developers seeking to broaden their AI development capabilities
  • Data analysts looking to apply AI techniques to complex datasets
  • R&D professionals involved in building AI-powered applications
 35 Hours

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