Professional Certificate in AI in Demand Planning and Forecasting
-- ViewingNowThe Professional Certificate in AI for Demand Planning and Forecasting is a comprehensive course that equips learners with essential skills to thrive in the age of data-driven decision making. This course emphasizes the importance of integrating Artificial Intelligence (AI) technologies, such as Machine Learning (ML) and Deep Learning (DL), into demand planning and forecasting processes.
4,963+
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 & Machine Learning: Understanding the basics of AI and machine learning concepts, algorithms, and techniques.
⢠Data Preparation for AI: Learning about data preprocessing, cleaning, and transformation techniques to prepare data for AI models.
⢠Demand Planning Basics: Exploring demand planning concepts, principles, and best practices, including forecasting, inventory management, and sales & operations planning.
⢠Time Series Analysis: Studying time series forecasting methods, including exponential smoothing, autoregressive integrated moving average (ARIMA), and seasonal decomposition.
⢠AI-driven Demand Forecasting: Leveraging AI techniques, including deep learning, neural networks, and reinforcement learning, for demand forecasting.
⢠AI Applications in Supply Chain Management: Examining AI use cases in supply chain management, including demand planning, inventory management, and logistics optimization.
⢠Natural Language Processing (NLP) for Demand Planning: Utilizing NLP techniques for sentiment analysis, opinion mining, and social media monitoring to inform demand planning decisions.
⢠AI Model Evaluation and Selection: Evaluating AI models' performance, selecting the best-fit model, and optimizing model parameters.
⢠AI Ethics and Bias in Demand Planning: Understanding ethical considerations and potential biases in AI-driven demand planning and forecasting.
⢠AI Implementation in Demand Planning: Best practices for AI implementation, including data governance, model deployment, and monitoring.
ę˛˝ë Ľ 경ëĄ
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
