If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
That's a wrap! Which natural language processing/ computer vision concepts do you know?
Like for more π
Share our channel with friends: https://t.iss.one/pythonspecialist
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
That's a wrap! Which natural language processing/ computer vision concepts do you know?
Like for more π
Share our channel with friends: https://t.iss.one/pythonspecialist
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Struggling with Machine Learning algorithms? π€
Then you better stay with me! π€
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! ππ»
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It is a binary classification model used to classify our input data into two main categories.
It can be extended to multiple classifications... but today we'll focus on a binary one.
Also known as Simple Logistic Regression.
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The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.
It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.
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For every parametric ML algorithm, we need a LOSS FUNCTION.
It is our map to find our optimal solution or global minimum.
(hoping there is one! π)
β ππ’π‘π¨π¦ - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.
Then you better stay with me! π€
We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! ππ»
1οΈβ£ ππ’πππ¦π§ππ π₯πππ₯ππ¦π¦ππ’π‘
It is a binary classification model used to classify our input data into two main categories.
It can be extended to multiple classifications... but today we'll focus on a binary one.
Also known as Simple Logistic Regression.
2οΈβ£ ππ’πͺ π§π’ ππ’π π£π¨π§π ππ§?
The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.
It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.
3οΈβ£ ππ’πͺ π§π’ πππππ‘π π§ππ πππ¦π§ πππ§?
For every parametric ML algorithm, we need a LOSS FUNCTION.
It is our map to find our optimal solution or global minimum.
(hoping there is one! π)
β ππ’π‘π¨π¦ - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.
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Data Science Projects
Photo
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Here are a few project ideas that could help you stand out:
Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.
Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.
Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.
Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.
Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.
By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.
Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.
Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.
Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.
Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.
Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.
Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.
By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.
Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.
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Hey guys,
What's up, what are you all working on or learning these days?
Let me know in comments ππ
What's up, what are you all working on or learning these days?
Let me know in comments ππ
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Hey guys,
What you all are planning to do this weekend?
My plan: Brush up Machine Learning and Statistics concepts π
What you all are planning to do this weekend?
My plan: Brush up Machine Learning and Statistics concepts π
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Which of the following is not a sampling technique?
Anonymous Quiz
14%
Simple Random sampling
13%
Systematic sampling
54%
Numerical Scientific sampling
20%
Stratified Sampling
π15
Data Science is very vast field.
I saw one linkedin profile today with below skills π
Technical Skills:
Data Manipulation: Numpy, Pandas, BeautifulSoup, PySpark
Data Visualization: EDA- Matplotlib, Seaborn, Plotly, Tableau, PowerBI
Machine Learning: Scikit-Learn, TimeSeries Analysis
MLOPs: Gensinms, Github Actions, Gitlab CI/CD, mlflows, WandB, comet
Deep Learning: PyTorch, TensorFlow, Keras
Natural Language Processing: NLTK, NER, Spacy, word2vec, Kmeans, KNN, DBscan
Computer Vision: openCV, Yolo-V5, unet, cnn, resnet
Version Control: Git, Github, Gitlab
Database: SQL, NOSQL, Databricks
Web Frameworks: Streamlit, Flask, FastAPI, Streamlit
Generative AI - HuggingFace, LLM, Langchain, GPT-3.5, and GPT-4
Project Management and collaboration tool- JIRA, Confluence
Deployment- AWS, GCP, Docker, Google Vertex AI, Data Robot AI, Big ML, Microsoft Azure
How many of them do you have?
I saw one linkedin profile today with below skills π
Technical Skills:
Data Manipulation: Numpy, Pandas, BeautifulSoup, PySpark
Data Visualization: EDA- Matplotlib, Seaborn, Plotly, Tableau, PowerBI
Machine Learning: Scikit-Learn, TimeSeries Analysis
MLOPs: Gensinms, Github Actions, Gitlab CI/CD, mlflows, WandB, comet
Deep Learning: PyTorch, TensorFlow, Keras
Natural Language Processing: NLTK, NER, Spacy, word2vec, Kmeans, KNN, DBscan
Computer Vision: openCV, Yolo-V5, unet, cnn, resnet
Version Control: Git, Github, Gitlab
Database: SQL, NOSQL, Databricks
Web Frameworks: Streamlit, Flask, FastAPI, Streamlit
Generative AI - HuggingFace, LLM, Langchain, GPT-3.5, and GPT-4
Project Management and collaboration tool- JIRA, Confluence
Deployment- AWS, GCP, Docker, Google Vertex AI, Data Robot AI, Big ML, Microsoft Azure
How many of them do you have?
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How to learn data science -> build projects
How to learn machine learning-> build projects
How to learn web development -> build projects
How to learn data analytics -> build projects
Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future.
Agree?
How to learn machine learning-> build projects
How to learn web development -> build projects
How to learn data analytics -> build projects
Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future.
Agree?
π26β€7π₯2
Google, Harvard, and even OpenAI are offering FREE Generative AI courses
ππ
https://t.iss.one/generativeai_gpt/26
ππ
https://t.iss.one/generativeai_gpt/26
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Do you guys believe in 80-20 rule (Pareto rule)?
Eg- For Data Scientist/ Analyst, 80% of time involve data cleaning and 20% actually doing analytics & delivering insights.
Add more in comments ππ
Eg- For Data Scientist/ Analyst, 80% of time involve data cleaning and 20% actually doing analytics & delivering insights.
Add more in comments ππ
π26β€1
Forwarded from AI Technology | ChatGPT & Nano Banana Prompts
Are you a free member and still havenβt had the GPT4-o rolled out to you yet?
Click this link and it should force it to roll out to you and become available!
Share this with anyone whoβs still waiting to try it out.
Join for more: https://t.iss.one/aijobss
Click this link and it should force it to roll out to you and become available!
Share this with anyone whoβs still waiting to try it out.
Join for more: https://t.iss.one/aijobss
π7π1