Explore the Best Free Online Artificial Intelligence Courses and Certifications in 2023, Syllabus, Books, and Benefits to help you choose the right program for your needs.
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| Artificial Intelligence Courses | Certifications | Syllabus | Books |
Why Artificial Intelligence Courses & Certifications Important?
Artificial Intelligence is a branch of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, and decision-making. While programming languages like Python, R, and Java are commonly used for building AI applications.
Artificial Intelligence Courses and certifications are important for several reasons:
- Artificial Intelligence Certifications can be advantageous during job interviews at preferred companies.
- Artificial Intelligence Certification helps in learning the new technologies, skills, and abilities for a specific promotion.
- A Artificial Intelligence certification course gives you a competitive advantage over others.
- It enhances prospects of promotion, raises and new job prospects.
- Keeps knowledge up to date.
Artificial Intelligence Courses & Certifications Topic & Syllabus?
Here is a general syllabus for learning Artificial Intelligence:
Week 1: Introduction to Artificial Intelligence
- Definition and brief history of AI
- Applications of AI in various fields
- Types of AI: Rule-based, Search-based, Machine Learning-based, and Deep Learning-based AI
Week 2: Mathematics for AI
- Linear Algebra: Vectors, Matrices, Eigenvalues, and Eigenvectors
- Probability Theory: Probability distributions, Bayes’ theorem, and Markov models
- Calculus: Derivatives and Gradients
- Statistics: Descriptive statistics, Inferential statistics, and Hypothesis testing
Week 3-4: Machine Learning
- Supervised Learning: Linear regression, Logistic regression, Decision trees, Random forests, and Naive Bayes classifier
- Unsupervised Learning: Clustering (K-Means, Hierarchical clustering), and Dimensionality reduction (PCA, t-SNE)
- Reinforcement Learning: Markov Decision Process, Q-learning, and Deep Reinforcement Learning
- Deep Learning: Neural networks, Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders
Week 5-6: Natural Language Processing
- Text Processing: Tokenization, Stop words removal, Stemming, and Lemmatization
- Text Classification: Sentiment Analysis, Topic modeling, and Document classification
- Language Generation: Neural Language Models, Text Generation, and Machine Translation
- Speech Recognition: Speech Processing, Feature extraction, and Acoustic Modeling
Week 7-8: Computer Vision
- Image Processing: Filtering, Edge detection, and Image enhancement
- Object Recognition: Feature extraction, Feature matching, and Object detection
- Object Tracking: Optical flow, Kalman filter, and Particle filter
- Image Segmentation: Thresholding, Clustering, and Edge-based segmentation
- Convolutional Neural Networks: Architecture, Transfer Learning, and Object Detection
Week 9-10: Robotics
- Robotics Fundamentals: Robot structure, Robot kinematics, and Robot dynamics
- Robot Perception: Sensor fusion, Visual perception, and Lidar perception
- Robot Control: PID controller, and Model predictive control
- Robot Planning: Path planning, Trajectory planning, and Motion planning
- Robot Learning: Imitation learning, Reinforcement learning, and Meta learning
Week 11-12: Ethics and Future of AI
- AI and Society: Social impact of AI, Job displacement, and Income inequality
- AI and Ethics: Bias and Fairness, Privacy, and Transparency
- AI Safety and Security: Adversarial attacks, Data poisoning, and Model stealing
- Future of AI: AI and Singularity, AI and Creativity, and AI and Human augmentation
Best Artificial Intelligence Books (High User Rating)
We recommend top Artificial Intelligence books to aid in learning Artificial Intelligence knowledge.
Useful Important Links
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