Get in Touch

Course Outline

Introduction to AI in Python

  • Core concepts and the scope of AI
  • Essential Python libraries for AI development
  • Structuring AI projects and defining workflows

Data Preparation for AI

  • Performing data cleaning, transformation, and feature engineering
  • Managing missing and imbalanced datasets
  • Applying feature scaling and encoding techniques

Supervised Learning Approaches

  • Understanding regression and classification algorithms
  • Employing ensemble methods such as Random Forest and Gradient Boosting
  • Conducting hyperparameter tuning and cross-validation

Unsupervised Learning Approaches

  • Exploring clustering methods like K-Means, DBSCAN, and hierarchical clustering
  • Reducing dimensionality with PCA and t-SNE
  • Identifying practical use cases for 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

Reinforcement Learning (Introduction)

  • Understanding core concepts: agents, environments, and rewards
  • Implementing fundamental reinforcement learning algorithms
  • Reviewing practical applications of reinforcement learning

Deployment of AI Models

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

Summary and Path Forward

Requirements

  • A strong grasp of Python programming fundamentals
  • Practical experience with data analysis libraries such as NumPy and pandas
  • Familiarity with basic machine learning concepts and algorithms

Target Audience

  • Software developers seeking to broaden their AI development capabilities
  • Data analysts looking to leverage AI techniques for complex dataset analysis
  • R&D professionals creating AI-driven applications
 35 Hours

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories