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Learn AI for free with courses covering the basics, machine learning, deep learning, generative AI, AI in practice, cloud AI services, and ethics.
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The estimated reading time for this document is approximately 8 minutes.
Persona
- Beginner AI Enthusiast - Data Science Student - Software Developer with AI Interest
1. Introduction to AI
- Course: IBM's "Introduction to AI"
- Topics:
- Basics of Artificial Intelligence
- History and evolution of AI
- Key concepts: Machine Learning, Deep Learning, Natural Language Processing
- Real-world applications of AI
- Ethical considerations in AI
2. Foundations of Machine Learning
- Course: Google Cloudâs "Machine Learning and AI Path"
- Topics:
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Data Preprocessing and Feature Engineering
- Model Training and Evaluation
- Introduction to TensorFlow and Keras
3. Deep Learning Essentials
- Course: MITâs "Professional Certificate Program in Machine Learning and AI"
- Topics:
- Neural Networks: Architecture, activation functions, backpropagation
- Convolutional Neural Networks (CNNs) for image processing
- Recurrent Neural Networks (RNNs) for sequential data
- Generative Adversarial Networks (GANs)
- Autoencoders and unsupervised learning in deep networks
4. Generative AI Techniques
- Course: Accenture's "Generative AI Nanodegree"
- Topics:
- Introduction to Generative AI
- Applications of Generative AI in art, music, and text generation
- Working with GPT models
- Ethical implications and challenges of generative AI
- Case studies and practical implementations
5. AI in Practice: Projects and Case Studies
- Course: Stanford's "Artificial Intelligence Professional Program"
- Topics:
- AI for healthcare, finance, and autonomous systems
- Advanced Natural Language Processing (NLP)
- Computer Vision applications
- AI-driven decision making and business strategy
- Capstone Project: End-to-End AI Solution Design
6. Cloud AI Services
- Course: Google Cloudâs "Machine Learning and AI Path"
- Topics:
- Introduction to Cloud AI services
- Deploying Machine Learning models on the cloud
- Using pre-trained models via APIs
- AutoML and Custom ML Models
- Real-time data processing with AI services
7. Ethics, Governance, and Future Trends in AI
- Course: MITâs "Professional Certificate Program in Machine Learning and AI"
- Topics:
- AI governance and policy-making
- Bias in AI models and fairness considerations
- Future trends in AI technology
- AI in society: Opportunities and risks
- Final reflections and ethical responsibility in AI