Compare AI training for customer analytics, marketing data and interpreting campaign results.
333 courses in this collection
Lazy Programmer Inc. , Lazy Programmer Team
Implement collaborative filtering, matrix factorization, neural models and ranking using NumPy/Keras/TensorFlow/Spark, broad technical recommender course is adjacent to personalization marketing
PracticalGrowth Learning
Create on-brand product/ad/subject copy, design web/email personalization and contextual recommendations/shopping flows, then plan pilots/KPIs/data/human review and governance. The focused ecommerce sequence differs from the same…
Christ Raharja
Clean customer reviews/tweets, analyze rating/sentiment correlation and positive/negative keywords, and compare TextBlob/EmoLex/VADER/NRCLex/BERT/Naive Bayes outputs, explicit customer/social datasets
Yaswanth Sai Palaghat
Use TextBlob to classify text sentiment and build a Flask interface, public description explicitly frames customer reviews/social opinions as applications. A technical NLP course transfers to feedback monitoring
Abhishek Kumar
Learn sentiment workflow and RNN/LSTM concepts, then implement LSTM sentiment modeling in Keras, the source names voice-of-customer reviews/surveys/social material as marketing/service applications
Taimoor khan
Preprocess/represent text, train/evaluate sentiment classifiers, apply lexicon and aspect-based methods and deep classifiers, source connects the workflow to customer wants and purchase-influencing online reviews
Meta Brains , Skool of AI
Build a customer-review sentiment pipeline and ecommerce review analysis combining entity recognition/topic modeling, with probabilistic features/Naive Bayes and model deployment. Broader NLP foundations accompany the explicit…
Dr. Mohammad Nauman
Implement a concise Keras/Python sentiment engine, develop the case-study model and extend it with CNNs, source targets understanding opinions about company products/services
Mammoth Interactive , John Bura
Build a positive/negative review classifier in a ten-lesson/79-minute project after Python/NumPy/pandas/ML/TensorFlow foundations, the whole course counts once and is adjacent technical training
Prof. Ryan Ahmed, PhD, MBA , SuperDataScience Team , Ligency
Apply logistic/SVM/KNN/tree/Naive Bayes models to targeted Facebook-ad response and Amazon Alexa review sentiment alongside healthcare/spam/other classification examples
VENDE-C PRO
Use practical prompt techniques for relevant customer communication, commercial data interpretation and each funnel stage, broader sales-process training is adjacent
Nia Joseph and Nitin Aggarwal
Define a marketing-agent use case with PILLAR, build with ACTION in Copilot Studio, optimize with RESULTS, personalize responses and apply responsible-AI controls, final project builds a marketing agent
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