(5 Ratings)

Artificial Intelligence

Categories: AI
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About Course

1. Introduction to Artificial Intelligence

Lesson 1: What is AI?

  • Defining artificial intelligence
  • Historical context and milestones
  • AI applications across industries

Lesson 2: Types of AI

  • Narrow (Weak) AI vs. General (Strong) AI
  • Exploring applied AI, cognitive AI, and beyond

Lesson 3: AI Problem Solving

  • Algorithms and heuristics
  • Problem-solving using AI techniques

2. Machine Learning Foundations

Lesson 1: Introduction to Machine Learning

  • Understanding the basics of machine learning
  • Supervised, unsupervised, and reinforcement learning

Lesson 2: Data and Preprocessing

  • Data collection and preprocessing techniques
  • Feature engineering and data augmentation

Lesson 3: Model Training and Evaluation

  • Selecting and training machine learning models
  • Cross-validation, overfitting, and model evaluation metrics

3. Deep Learning Principles

Lesson 1: Neural Networks

  • Building blocks of neural networks
  • Activation functions, layers, and architectures

Lesson 2: Training Deep Neural Networks

  • Backpropagation and gradient descent
  • Optimizers, loss functions, and regularization techniques

Lesson 3: Convolutional Neural Networks (CNNs)

  • Understanding CNNs for image analysis
  • Image classification and object detection using CNNs

4. Natural Language Processing

Lesson 1: Introduction to NLP

  • Basics of natural language processing
  • Text preprocessing and tokenization

Lesson 2: Text Representation

  • Word embeddings and vectorization
  • TF-IDF and word2vec techniques

Lesson 3: NLP Applications

  • Sentiment analysis, named entity recognition
  • Machine translation and chatbots

5. Computer Vision

Lesson 1: Introduction to Computer Vision

  • Fundamentals of computer vision
  • Image processing and feature extraction

Lesson 2: Image Classification and Object Detection

  • Image classification techniques
  • Object detection using region-based and YOLO algorithms

Lesson 3: Image Segmentation and CNNs in Vision

  • Semantic and instance segmentation
  • Applying CNNs to solve complex vision tasks

6. AI Ethics and Future Trends

Lesson 1: AI Ethics and Bias

  • Ethical considerations in AI development
  • Addressing bias and fairness issues

Lesson 2: AI in Society and Work

  • Impact of AI on industries and jobs
  • Collaborating with AI systems

Lesson 3: Future Trends in AI

  • Exploring the future of AI technology
  • AI advancements and their potential implications

7. Final Exam

  • A comprehensive exam covering all course topics
  • Multiple-choice, true/false, and short answer questions
  • Prepare thoroughly for your AI knowledge assessment!
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What Will You Learn?

  • you'll learn the core concepts of Artificial Intelligence (AI). Starting with an introduction to AI's history and types, you'll dive into problem-solving techniques and explore machine learning foundations, including data preprocessing and model training. The course delves into deep learning principles, covering neural networks, training methods, and convolutional neural networks (CNNs) for image analysis.
  • Moving to Natural Language Processing (NLP), you'll discover text preprocessing, word embeddings, and NLP applications like sentiment analysis and machine translation. The course also addresses Computer Vision, teaching image processing, image classification, object detection, and segmentation.
  • Ethical considerations and AI's impact on society and jobs are discussed, along with future trends in AI. The course culminates in a final exam that tests your understanding across all topics. Whether you're new to AI or seeking to deepen your knowledge, this course offers a comprehensive and practical grasp of AI concepts and applications.

Student Ratings & Reviews

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5 months ago
The flexibility and accessibility of the materials made it easy for me to balance work and learning.
5 months ago
The course covered a wide range of topics, which was great, but it felt a bit rushed at times.
5 months ago
The hands-on projects were both fun and educational. I feel much more confident in my skills after completing this course.
5 months ago
The course had some great content, but it could use some updates to stay current with industry trends.
7 months ago
I found the course content to be well-structured and easy to understand.