Find Your Competitive Advantage With AI
Leverage time-tested AI strategies, application ideas, and best practices from the field
to lead a competitive and profitable business with AI
Read The Business Case For AI
“This is a great executive and leadership level book about AI for today. I like how Kavita sets the context at the beginning of what AI is and isn’t in its current state. The “Get the Ideas Flowing” part definitely did that for me. I’ve always thought of AI as what we did with the data at the end but in the section on using AI to set the stage for intelligent data analytics to enrich and standardize your data and then feed that into business intelligence dashboards, you may already have in place to run your business really got me thinking. This book is definitely needed and should be on your bookshelf.”—Amazon Reader
Read by leaders and practitioners from:
Ernst & Young
Universiti Sains Malaysia
Canadian Govt. Agencies
Kavita Ganesan is an AI practitioner, advisor, educator, and founder of Opinosis Analytics. She advises senior management and technology teams across the enterprise to help them successfully integrate AI for meaningful business outcomes.
With over 15 years of experience, Kavita has managed, scaled, and delivered multiple successful AI initiatives for fortune 500 companies as well as smaller organizations. She has also helped leaders and practitioners around the world through her blog posts, coaching sessions, and open-source tools.
Kavita holds degrees from prestigious computer science programs, specifically a Masters degree from the University of Southern California and a Ph.D. from the University of Illinois at Urbana Champaign, with a specialization in NLP, Machine Learning, and Information Retrieval (Search).
Kavita has been featured as an expert by numerous media outlets, including Forbes, CEOWorld, CMSWire, Verizon, SDTimes, Techopedia, and Ted Magazine. To learn more about her frameworks and ideas, get her AI book for business leaders.
AI Integration & Applications for Business
Machine Learning & NLP Development Tutorials
A Gentle Introduction to Deep Neural Networks with Python
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Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI
Word2Vec is a widely used word representation technique that uses…
HashingVectorizer vs. CountVectorizer
Previously, we learned how to use CountVectorizer for text processing….
10+ Examples for Using CountVectorizer
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Build Your First Text Classifier in Python with Logistic Regression
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Multi-factor clustering for a marketplace search interface
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Findilike: Preference Driven Entity Search
Traditional web search engines enable users to find documents based on topics. However, in finding entities such as restaurants, hotels and products, traditional search engines fail to suffice as users…
Linguistic Understanding of Complaints and Praises in User Reviews
This is a short study paper that categorizes positive and negative review sentences into 4 categories: positive only, praise, negative only and complaint. The intuition is that praise sentences and…