for presentation

1. WACHEMO UNIVERSITY
DURAME CAMPUS

2. Learning Probabilistic Models
• Learning probabilistic models: refers to the process of estimating the
parameters or structure of a probabilistic model from observed data
• learning probabilistic models, the term "learning" typically refers to the
estimation of model parameters, which capture the statistical properties of
the data

3. What Is Probabilistic Modeling?
• Probabilistic modeling is a statistical approach that uses the effect of
random occurrences or actions to forecast the possibility of future results
• Considers new situations and a wide range of uncertainty while not
underestimating dangers
• The three primary building blocks of probabilistic modeling are adequate
probability distributions, correct use of input information for these
distribution functions, and proper accounting for the linkages and
interactions between variables

4. Process of Learning Probabilistic models
• Model Selection: The first step is to choose an appropriate probabilistic model that can capture
the relationships and dependencies in the data
• Data Collection:The next step is to collect a representative dataset that captures the relevant
information about the problem being modeled
• Parameter Initialization: Before learning, the model parameters need to be initialized
• Estimation Algorithm: an estimation algorithm is used to update the model parameters based
on the observed data
• Model Evaluation: After the learning process, it is important to evaluate the quality of the
learned model

5. Process of Learning Probabilistic models
• Model Refinement: If the learned model does not perform satisfactorily,
further refinement or optimization steps may be necessary

6. Importance of Learning Probabilistic Models
• Learning probabilistic models is of great importance in various domains and
applications
• Probabilistic modeling enables more accurate predictions, robust analysis,
and better understanding of the data, leading to improved decision-making
and actionable insights in various domains
• Probabilistic models are capable of handling such data by capturing the
inherent noise and variability

7. Importance of Learning Probabilistic Models
• Probabilistic models offer a powerful approach to leverage prior knowledge
or beliefs when dealing with limited data
• This adaptability makes probabilistic models suitable for tasks such as
online learning, domain adaptation, and transfer learning

8. Examples of Learning Probabilistic Models
• Naive Bayes Classifier

9. Examples of Learning Probabilistic Models
• Hidden Markov Models

10. Examples of Learning Probabilistic Models
• Gaussian Mixture Models

11. Examples of Learning Probabilistic Models
• Probabilistic Graphical Models

12. Advantages of learning
Probabilistic Models

13. Probabilistic models provide a natural framework for quantifying
and reasoning about uncertainty. By representing uncertainty
using probability distributions, probabilistic models can estimate
the likelihood of different outcomes and provide measures of
confidence or uncertainty in predictions or inferences. This is
particularly valuable when making decisions under uncertainty or
when dealing with noisy or incomplete data

14. • Probabilistic models offer flexibility in modeling complex
relationships and capturing dependencies between variables.
They can handle a wide range of data types, including
continuous, discrete, and mixed data, as well as handle various
types of relationships such as linear, nonlinear, and hierarchical
relationships. This versatility allows for modeling real-world
phenomena in a more realistic and expressive manner

15. Probabilistic models provide a principled way to incorporate prior
knowledge or beliefs into the modeling process. By specifying prior
distributions over model parameters or structure, prior knowledge
can be explicitly encoded and combined with observed data to
obtain more robust and accurate estimates. This is particularly
useful in situations where prior information is available or when
dealing with limited data

16. Probabilistic models can handle missing data effectively. They can model the
messiness mechanism and estimate the missing values based on the observed
data and the estimated model parameters. This is particularly valuable in
situations where data may be missing due to various reasons, such as
incomplete measurements or data collection processes

17. • It offers critical data for operational and strategic decision-making
processes
• It may be utilized in a flexible and integrated manner for probabilistic load-
flow assessments, reliability analyses, voltage sag evaluation, and general
scenario analysis
• One of the most important advantages of probabilistic analysis is that it
allows managers to participate in meaningful discourse about their risks

18. Conclusion
• Probabilistic Models are a great way to understand the trends that can be
derived from the data and create predictions for the future
• As one of the first topics that is taught in Machine Learning, the importance
of probabilistic models is understated
• These models provide a foundation for the machine learning models to
understand the prevalent trends and their behavior

19. Natural language processing
• Natural language processing is the ability of a computer program to
understand human language as it's spoken and written referred to as natural
language
• NLP has existed for more than 50 years and has roots in the field of
linguistics
• NLP uses either rule-based or machine learning approaches to understand
the structure and meaning of text

20. How does natural language processing work?
• Tokenization: Tokenization substitutes sensitive information with no
sensitive information, or a token
• Stop word removal: Common words are removed from the text, so unique
words that offer the most information about the text remain
• Lemmatization and stemming: Lemmatization groups together different
inflected versions of the same word
• Part-of-speech tagging: Words are tagged based on which part of speech
they correspond to such as nouns, verbs or adjectives

21. How does natural language processing work?
• Rule-based system:This system uses carefully designed linguistic rules
• Machine learning-based system: Machine learning algorithms use statistical
methods

22. Why is natural language processing important?
• The advantages of natural language processing can be seen when
considering the following two statements: "Cloud computing insurance
should be part of every service-level agreement" and "A good SLA ensures
an easier night's sleep even in the cloud."
• If a user relies on natural language processing for search, the program will
recognize that cloud computing is an entity, that cloud is an abbreviated
form of cloud computing, and that SLA is an industry acronym for service-
level agreement
• Likewise, NLP is useful for the same reasons as when a person interacts with
a generative AI Chatbot or AI voice assistant

23. Techniques and methods of natural language
processing
• Parsing:This is the grammatical analysis of a sentence
• Word segmentation: This is the act of taking a string of text and deriving
word forms from it
• Sentence breaking:This places sentence boundaries in large texts
• Morphological segmentation:This divides words into smaller parts called
morphemes
• Stemming: This divides words with inflection in them into root forms

24. Techniques and methods of natural language
processing
• Word sense disambiguation:This derives the meaning of a word based on
context
• Named entity recognition : NER determines words that can be categorized
into groups
• Natural language generation : NLG uses a database to determine the
semantics behind words and generate new text

25. What is natural language processing used for?
• Text classification: This function assigns tags to texts to put them in
categories
• Text extraction: This function automatically summarizes text and finds
important pieces of data
• Machine translation: In this process, a computer translates text from one
language, such as English, to another language, such as French, without
human intervention
• Natural language generation: This process uses natural language processing
algorithms to analyze unstructured data and automatically produce content
based on that data
• Customer feedback analysis: Tools using AI can analyze social media reviews
and filter out comments and queries for a company

26. What is natural language processing used for?
• Customer service automation: Voice assistants on a customer service phone
line can use speech recognition to understand what the customer is saying,
so that it can direct their call correctly
• Automatic translation: Tools such as GoogleTranslate, BingTranslator and
Translate Me can translate text, audio and documents into another
language
• Academic research and analysis: Tools using AI can analyze huge amounts of
academic material and research papers based on the metadata of the text
as well as the text itself
• Analysis and categorization of healthcare records: AI-based tools can use
insights to predict and, ideally, prevent disease

27. What is natural language processing used for?
• Plagiarism detection
• Stock forecasting and insights into financial trading
• Talent recruitment in human resourcesAutomation of
routine litigation:
• Spam detection

28. Benefits of natural language processing
• Offers improved accuracy and efficiency of documentation
• Enables an organization to use chatbots for customer support
• Provides an organization with the ability to automatically make a readable
summary of a larger, more complex original text
• Let’s organizations analyze structured and unstructured data
• Enables personal assistants such as Alexa to understand the spoken word
• Makes it easier for organizations to perform sentiment analysis
• Organizations can use NLP to better understand lead generation, social
media posts, surveys and reviews
• Provides advanced insights from analytics that were previously unreachable
due to data volume

29. Challenges of natural language processing
• Precision: Computers traditionally require humans to speak to them in a
programming language that's precise, unambiguous and highly structured --
or through a limited number of clearly enunciated voice commands
• Tone of voice and inflection: Natural language processing hasn't yet been
perfected
• Evolving use of language: Natural language processing is also challenged by
the fact that language -- and the way people use it -- is continually changing
• Bias: NLP systems can be biased when their processes reflect the biases
that appear in their training data