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コース概要
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
要求
- 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 時間
お客様の声 (3)
私たちのプロジェクトで使用しているデータ（ラスター形式の衛星画像）とより類似したデータを使用して、より実践的な演習を行えること
Matthieu - CS Group
コース - Scaling Data Analysis with Python and Dask
機械翻訳
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
コース - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
