Course Outline

Overview of AI in Python

  • Key concepts and scope of AI
  • Python libraries for AI development
  • AI project structure and workflow

Data Preparation for AI

  • Data cleaning, transformation, and feature engineering
  • Handling missing and unbalanced data
  • Feature scaling and encoding

Supervised Learning Techniques

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

Unsupervised Learning Techniques

  • Clustering methods: K-Means, DBSCAN, hierarchical clustering
  • Dimensionality reduction: PCA, t-SNE
  • Use cases for unsupervised learning

Neural Networks and Deep Learning

  • Introduction to TensorFlow and Keras
  • Building and training feedforward neural networks
  • Optimizing neural network performance

Reinforcement Learning (Intro)

  • Core concepts of agents, environments, and rewards
  • Implementing basic reinforcement learning algorithms
  • Applications of reinforcement learning

Deploying AI Models

  • Saving and loading trained models
  • Integrating models into applications via APIs
  • Monitoring and maintaining AI systems in production

Summary and Next Steps

Requirements

  • Solid understanding of Python programming fundamentals
  • Experience with data analysis libraries such as NumPy and pandas
  • Basic knowledge of machine learning concepts and algorithms

Audience

  • Software developers aiming to expand their AI development skills
  • Data analysts seeking to apply AI techniques to complex datasets
  • R&D professionals building AI-powered applications
 35 Hours

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