Short AI Course by TTC

The Training Company Courses Terms and Facilities

1. Introduction to Artificial Intelligence
   - Definition and brief history of AI
   - Applications and impact of AI in various fields

2. Problem Solving and Search Algorithms
   - Problem formulation and goal setting
   - Uninformed search algorithms (BFS, DFS)
   - Heuristic search algorithms (A*, Greedy search)
   - Constraint satisfaction problems

3. Knowledge Representation and Reasoning
   - Propositional logic and predicate logic
   - Knowledge representation using semantic networks, frames, and ontologies
   - Inference and deduction using logical reasoning

4. Machine Learning Basics
   - Introduction to machine learning and its types
   - Supervised learning: regression and classification
   - Unsupervised learning: clustering and dimensionality reduction
   - Evaluation and validation of machine learning models

5. Neural Networks and Deep Learning
   - Basics of artificial neural networks
   - Activation functions and gradient descent
   - Deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks)
   - Training and fine-tuning deep learning models

6. Natural Language Processing
   - Introduction to NLP and its applications
   - Text preprocessing techniques
   - Language modeling and sentiment analysis
   - Named entity recognition and part-of-speech tagging

7. Reinforcement Learning
   - Introduction to reinforcement learning
   - Markov Decision Processes and Q-learning
   - Exploration-exploitation trade-off
   - Deep reinforcement learning and applications

8. AI Ethics and Social Implications
   - Ethical considerations in AI development and deployment
   - Bias and fairness in AI algorithms
   - Privacy and security concerns
   - AI's impact on the job market and society

9. AI in Practice
   - Real-world case studies and applications of AI
   - Industry-specific AI implementations
   - AI project management and deployment considerations

10. Future Trends in AI
    - Cutting-edge developments and research areas in AI
    - Emerging technologies (e.g., explainable AI, quantum computing)
    - The future of AI and its potential challenges and opportunities


What is AI

artificial intelligence

artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

What is Machine Learning

https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

Big data in AI

https://online.maryville.edu/blog/big-data-is-too-big-without-ai/

Some AI Terms

https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_terminology.htm

What is an AI enabled company ?

https://analyticsindiamag.com/whats-the-difference-between-true-ai-ai-enabled-companies/

https://www.entrepreneur.com/article/381442

What machine learning can and cannot do

https://medium.com/@stefanovskyi/what-machine-learning-can-do-and-what-can-not-difficult-stuff-in-simple-words-ef9f245dd6f6

What is deep learning

https://medium.com/@labai/machine-learning-vs-deep-learning-a-non-technical-introduction-d2cdce6a953f

https://enterprisersproject.com/article/2019/7/deep-learning-explained-plain-english

Workflow of a machine learning project

https://www.run.ai/guides/machine-learning-engineering/machine-learning-workflow

https://towardsdatascience.com/workflow-of-a-machine-learning-project-ec1dba419b94

Workflow of a data science project

https://www.datascience-pm.com/data-science-workflow/

https://www.kdnuggets.com/2020/07/laymans-guide-data-science-workflow.html

https://neptune.ai/blog/best-practices-for-data-science-project-workflows-and-file-organizations

Data analyst skills

https://www.dataquest.io/blog/data-analyst-skills/

How to choose an AI project

https://towardsdatascience.com/how-to-pick-a-successful-ai-project-part-1-finding-the-problem-and-collecting-data-9e701e316977

How to make an AI team

https://www.gartner.com/smarterwithgartner/how-to-staff-your-ai-team

Technical tools for AI teams

https://www.devteam.space/blog/the-best-ai-tools/

Some AI Case studies

https://www.datamation.com/artificial-intelligence/artificial-intelligence-use-cases/

Roles of an AI team

https://towardsai.net/p/l/roles-of-an-ai-team

AI Transformation Playbook

https://landing.ai/resources/ai-transformation-playbook/

AI pitfalls to avoid

https://learn.filtered.com/thoughts/ai-in-learning-and-development-pitfalls

https://towardsdatascience.com/5-ai-pitfalls-for-business-how-to-avoid-them-fd619d2860c4

How to get started in AI

https://bigdata-madesimple.com/want-get-started-artificial-intelligence-7-easy-steps/

AI application areas

https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/artificial-intelligence-applications

AI techniques

https://www.analyticssteps.com/blogs/6-major-branches-artificial-intelligence-ai

A realistic view of AI

https://discover.hubpages.com/technology/a-realistic-view-on-how-artificial-intelligence-works

Discrimination and Bias in AI

https://technologyandsociety.org/bias-and-discrimination-in-ai-a-cross-disciplinary-perspective/

Adversarial attacks on AI

https://venturebeat.com/2021/05/29/adversarial-attacks-in-machine-learning-what-they-are-and-how-to-stop-them/

Adverse uses of AI

https://bernardmarr.com/what-are-the-negative-impacts-of-artificial-intelligence-ai/

How AI can help developing economies

https://blogs.worldbank.org/digital-development/how-ai-can-help-developing-countries-rebuild-after-pandemic

AI and future jobs

https://www.forbes.com/sites/ashleystahl/2021/03/10/how-ai-will-impact-the-future-of-work-and-life/?sh=67dd069d79a3