Professional Certificate in AI IP: Actionable Knowledge
-- ViewingNowThe Professional Certificate in AI Intellectual Property (AI IP): Actionable Knowledge course is a comprehensive program designed to equip learners with the essential skills necessary for career advancement in the AI industry. This course is of paramount importance as it bridges the gap between AI technology and IP law, providing learners with a unique understanding of the legal and ethical implications of AI.
2,512+
Students enrolled
GBP £ 140
GBP £ 202
Save 44% with our special offer
ě´ ęłźě ě ëí´
100% ě¨ëźě¸
ě´ëěë íěľ
ęłľě ę°ëĽí ě¸ěŚě
LinkedIn íëĄíě ěśę°
ěëŁęšě§ 2ę°ě
죟 2-3ěę°
ě¸ě ë ěě
ë기 ę¸°ę° ěě
ęłźě ě¸ëśěŹí
⢠Introduction to AI IP: Understanding the basics of AI intellectual property, including patents, trademarks, copyrights, and trade secrets.
⢠Patent Law and AI: A deep dive into patent law and its intersection with AI, including eligibility, novelty, non-obviousness, and enablement.
⢠Trademarks and AI: Learning about trademarks and how they apply to AI, including the registration process, infringement, and licensing.
⢠Copyright and AI: Understanding copyright law and its application to AI, including originality, fixation, and ownership.
⢠Trade Secrets and AI: Exploring trade secrets and their relevance to AI, including protection, misappropriation, and licensing.
⢠AI Ethics and IP: Examining the ethical considerations surrounding AI and intellectual property, including ownership, access, and fairness.
⢠AI IP Licensing: Learning about licensing in the context of AI intellectual property, including types of licenses, negotiation, and enforcement.
⢠AI IP Litigation: Understanding the litigation process for AI intellectual property disputes, including the burden of proof, defenses, and remedies.
⢠AI IP Strategy: Developing a comprehensive AI intellectual property strategy, including identifying assets, protecting rights, and leveraging value.
ę˛˝ë Ľ 경ëĄ
Data Scientists analyze and interpret complex digital data to help companies make decisions. 2. **Software Developer (20%)**
Software Developers design, create, test, and maintain software systems. 3. **Machine Learning Engineer (18%)**
Machine Learning Engineers design and build machine learning systems that can learn and adapt automatically. 4. **AI Engineer (15%)**
AI Engineers focus on creating and maintaining artificial intelligence applications. 5. **Business Intelligence Developer (12%)**
Business Intelligence Developers design and build data tools for business analysis purposes. 6. **Data Analyst (10%)**
Data Analysts collect, process, and perform statistical analyses of data.
ě í ěęą´
- 죟ě ě ëí 기본 ě´í´
- ěě´ ě¸ě´ ëĽěë
- ěť´í¨í° ë° ě¸í°ëˇ ě ꡟ
- 기본 ěť´í¨í° 기ě
- ęłźě ěëŁě ëí íě
ěŹě ęłľě ěę˛Šě´ íěíě§ ěěľëë¤. ě ꡟěąě ěí´ ě¤ęłë ęłźě .
ęłźě ěí
ě´ ęłźě ě ę˛˝ë Ľ ę°ë°ě ěí ě¤ěŠě ě¸ ě§ěęłź 기ě ě ě ęłľíŠëë¤. ꡸ę˛ě:
- ě¸ě ë°ě 기ę´ě ěí´ ě¸ěŚëě§ ěě
- ęśíě´ ěë 기ę´ě ěí´ ęˇě ëě§ ěě
- ęłľě ě겊ě ëł´ěě
ęłźě ě ěąęłľě ěźëĄ ěëŁí늴 ěëŁ ě¸ěŚě뼟 ë°ę˛ ëŠëë¤.
ě ěŹëë¤ě´ ę˛˝ë Ľě ěí´ ě°ëŚŹëĽź ě ííëę°
댏롰 ëĄëŠ ě¤...
ě죟 돝ë ě§ëŹ¸
ě˝ě¤ ěę°ëŁ
- 죟 3-4ěę°
- 쥰기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- 죟 2-3ěę°
- ě 기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- ě 체 ě˝ě¤ ě ꡟ
- ëě§í¸ ě¸ěŚě
- ě˝ě¤ ěëŁ
ęłźě ě ëł´ ë°ę¸°
íěŹëĄ ě§ëś
ě´ ęłźě ě ëšěŠě ě§ëśí기 ěí´ íěŹëĽź ěí ě˛ęľŹě뼟 ěě˛íě¸ě.
ě˛ęľŹěëĄ ę˛°ě ę˛˝ë Ľ ě¸ěŚě íë
