Concepts:

1. Natural Language Processing,

2. Computer Vision,

3. Machine Learning,

4. Deep Learning,

5. Recommender System,

6. Database,

7. Web Technology

Technical:

1. TF-IDF: TF-IDF stands for “Term Frequency — Inverse Document Frequency”. This is a technique to quantify words in a set of documents. We generally compute a score for each word to signify its importance in the document and corpus. This method is a widely used technique in Information Retrieval and Text Mining.

2. Word2Vec: Word2vec is a method to efficiently create word embeddings and has been around since 2013. But in addition to its utility as a word-embedding method, some of its concepts have been shown to be effective in creating recommendation engines and making sense of sequential data even in commercial, non-language tasks. Companies like Airbnb, Alibaba, Spotify, and Anghami have all benefitted from carving out this brilliant piece of machinery from the world of NLP and using it in production to empower a new breed of recommendation engines.

3. SpaCy: spaCy is a free and open-source library for Natural Language Processing (NLP) in Python with a lot of in-built capabilities. It’s becoming increasingly popular for processing and analyzing data in NLP. Unstructured textual data is produced at a large scale, and it’s important to process and derive insights from unstructured data. To do that, you need to represent the data in a format that can be understood by computers. NLP can help you do that.

4. Text Analysis: Text analysis (TA) is a machine learning technique used to automatically extract valuable insights from unstructured text data. Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights. You can us text analysis to extract specific information, like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic.

5. Knowledge Representation:

Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent.

Knowledge Representation and Reasoning (KR, KRR) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language. Knowledge representation in AI is not just about storing data in a database, it allows a machine to learn from that knowledge and behave intelligently like a human being.

6. Linear Regression:

7. Support Vector Machine,

8. K-Means Clustering,

9. Random Forest,

10. CNN,

11. RNN,

12. LSTM,

13. Image and Video Analysis - Facial Expression and Gesture Recognition

References:

https://towardsdatascience.com/tf-idf-for-document-ranking-from-scratch-in-python-on-real-world-dataset-796d339a4089

https://realpython.com/natural-language-processing-spacy-python/

https://monkeylearn.com/text-analysis/