๐Here are 5 fresh Project ideas for Data Analysts ๐
๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
Join for more -> https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
ENJOY LEARNING ๐๐
๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
Join for more -> https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
ENJOY LEARNING ๐๐
๐6โค1
Top 5 data science projects for freshers
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
๐2โค1
sql-basics-cheat-sheet-a4.pdf
120.5 KB
SQL Basics Cheat Sheet
LearnSQL, 2022
LearnSQL, 2022
๐4โค2
โEssential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
1. Data Types and Structures:
โข Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
โข Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
โข Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
โข Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
โข Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
โข Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
โข Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
โข Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
โข Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
โข Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
โข Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
โข Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
โข Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
โข Outlier Detection and Removal: Identifying and addressing extreme values
โข Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
โข Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
โข Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
โข Data Privacy and Security: Protecting sensitive information
โข Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
โข Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
โข R: Statistical programming language with strong visualization capabilities
โข SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
โข Hadoop and Spark: Frameworks for processing massive datasets
โข Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
โข Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
โข Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
โข Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐5
Complete Roadmap to learn Machine Learning and Artificial Intelligence
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐2
Machine learning powers so many things around us โ from recommendation systems to self-driving cars!
But understanding the different types of algorithms can be tricky.
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
๐. ๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
๐๐จ๐ฆ๐ ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Linear Regression โ For predicting continuous values, like house prices.
โก๏ธ Logistic Regression โ For predicting categories, like spam or not spam.
โก๏ธ Decision Trees โ For making decisions in a step-by-step way.
โก๏ธ K-Nearest Neighbors (KNN) โ For finding similar data points.
โก๏ธ Random Forests โ A collection of decision trees for better accuracy.
โก๏ธ Neural Networks โ The foundation of deep learning, mimicking the human brain.
๐. ๐๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
With unsupervised learning, the model explores patterns in data that doesnโt have any labels. It finds hidden structures or groupings.
๐๐จ๐ฆ๐ ๐ฉ๐จ๐ฉ๐ฎ๐ฅ๐๐ซ ๐ฎ๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ K-Means Clustering โ For grouping data into clusters.
โก๏ธ Hierarchical Clustering โ For building a tree of clusters.
โก๏ธ Principal Component Analysis (PCA) โ For reducing data to its most important parts.
โก๏ธ Autoencoders โ For finding simpler representations of data.
๐. ๐๐๐ฆ๐ข-๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
๐๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐๐ฆ๐ข-๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Label Propagation โ For spreading labels through connected data points.
โก๏ธ Semi-Supervised SVM โ For combining labeled and unlabeled data.
โก๏ธ Graph-Based Methods โ For using graph structures to improve learning.
๐. ๐๐๐ข๐ง๐๐จ๐ซ๐๐๐ฆ๐๐ง๐ญ ๐๐๐๐ซ๐ง๐ข๐ง๐
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
๐๐จ๐ฉ๐ฎ๐ฅ๐๐ซ ๐ซ๐๐ข๐ง๐๐จ๐ซ๐๐๐ฆ๐๐ง๐ญ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Q-Learning โ For learning the best actions over time.
โก๏ธ Deep Q-Networks (DQN) โ Combining Q-learning with deep learning.
โก๏ธ Policy Gradient Methods โ For learning policies directly.
โก๏ธ Proximal Policy Optimization (PPO) โ For stable and effective learning.
ENJOY LEARNING ๐๐
But understanding the different types of algorithms can be tricky.
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
๐. ๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
๐๐จ๐ฆ๐ ๐๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Linear Regression โ For predicting continuous values, like house prices.
โก๏ธ Logistic Regression โ For predicting categories, like spam or not spam.
โก๏ธ Decision Trees โ For making decisions in a step-by-step way.
โก๏ธ K-Nearest Neighbors (KNN) โ For finding similar data points.
โก๏ธ Random Forests โ A collection of decision trees for better accuracy.
โก๏ธ Neural Networks โ The foundation of deep learning, mimicking the human brain.
๐. ๐๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
With unsupervised learning, the model explores patterns in data that doesnโt have any labels. It finds hidden structures or groupings.
๐๐จ๐ฆ๐ ๐ฉ๐จ๐ฉ๐ฎ๐ฅ๐๐ซ ๐ฎ๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ K-Means Clustering โ For grouping data into clusters.
โก๏ธ Hierarchical Clustering โ For building a tree of clusters.
โก๏ธ Principal Component Analysis (PCA) โ For reducing data to its most important parts.
โก๏ธ Autoencoders โ For finding simpler representations of data.
๐. ๐๐๐ฆ๐ข-๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
๐๐จ๐ฆ๐ฆ๐จ๐ง ๐ฌ๐๐ฆ๐ข-๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Label Propagation โ For spreading labels through connected data points.
โก๏ธ Semi-Supervised SVM โ For combining labeled and unlabeled data.
โก๏ธ Graph-Based Methods โ For using graph structures to improve learning.
๐. ๐๐๐ข๐ง๐๐จ๐ซ๐๐๐ฆ๐๐ง๐ญ ๐๐๐๐ซ๐ง๐ข๐ง๐
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
๐๐จ๐ฉ๐ฎ๐ฅ๐๐ซ ๐ซ๐๐ข๐ง๐๐จ๐ซ๐๐๐ฆ๐๐ง๐ญ ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ ๐ข๐ง๐๐ฅ๐ฎ๐๐:
โก๏ธ Q-Learning โ For learning the best actions over time.
โก๏ธ Deep Q-Networks (DQN) โ Combining Q-learning with deep learning.
โก๏ธ Policy Gradient Methods โ For learning policies directly.
โก๏ธ Proximal Policy Optimization (PPO) โ For stable and effective learning.
ENJOY LEARNING ๐๐
๐5
You don't need to buy a GPU for machine learning work!
There are other alternatives. Here are some:
1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim
Spend your time focusing on your problem.๐ช๐ช
There are other alternatives. Here are some:
1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim
Spend your time focusing on your problem.๐ช๐ช
๐5๐1
Useful Telegram Channels to boost your career in 2025 ๐๐
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LLM_foundation.pdf
2.7 MB
Foundational Large Language Models & Text Generation
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Hello everyone here are some tableau projects along with the datasets to work on
1. Sales Performance Dashboard:
- Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
- UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)
2. Customer Segmentation Analysis:
- Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
- UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)
3. Inventory Management Dashboard:
- Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
- Data.gov: [Inventory datasets](https://www.data.gov/)
4. Financial Analysis Dashboard:
- Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
- Quandl: [Financial datasets](https://www.quandl.com/)
5. Social Media Analytics Dashboard:
- Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
- Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)
6. Website Analytics Dashboard:
- Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
- SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)
7. Supply Chain Analysis Dashboard:
- Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
- Data.gov: [Supply chain datasets](https://www.data.gov/)
8. Healthcare Analytics Dashboard:
- CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
- HealthData.gov: [Healthcare datasets](https://healthdata.gov/)
9. Employee Performance Dashboard:
- Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
- Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)
10. Real-time Dashboard:
- Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
- Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.
All the best for your career โค๏ธ
1. Sales Performance Dashboard:
- Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
- UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)
2. Customer Segmentation Analysis:
- Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
- UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)
3. Inventory Management Dashboard:
- Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
- Data.gov: [Inventory datasets](https://www.data.gov/)
4. Financial Analysis Dashboard:
- Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
- Quandl: [Financial datasets](https://www.quandl.com/)
5. Social Media Analytics Dashboard:
- Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
- Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)
6. Website Analytics Dashboard:
- Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
- SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)
7. Supply Chain Analysis Dashboard:
- Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
- Data.gov: [Supply chain datasets](https://www.data.gov/)
8. Healthcare Analytics Dashboard:
- CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
- HealthData.gov: [Healthcare datasets](https://healthdata.gov/)
9. Employee Performance Dashboard:
- Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
- Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)
10. Real-time Dashboard:
- Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
- Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.
All the best for your career โค๏ธ
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Remember: Tough times are opportunities to practice virtue.
Courage, justice, wisdom, self-control. They're forged in fire.
Courage, justice, wisdom, self-control. They're forged in fire.
Important Machine Learning Algorithms ๐๐
- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)
Like this post if you want me to explain each algorithm in detail
Share with credits: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)
Like this post if you want me to explain each algorithm in detail
Share with credits: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
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Hello everyone here are some tableau projects along with the datasets to work on
1. Sales Performance Dashboard:
- Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
- UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)
2. Customer Segmentation Analysis:
- Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
- UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)
3. Inventory Management Dashboard:
- Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
- Data.gov: [Inventory datasets](https://www.data.gov/)
4. Financial Analysis Dashboard:
- Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
- Quandl: [Financial datasets](https://www.quandl.com/)
5. Social Media Analytics Dashboard:
- Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
- Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)
6. Website Analytics Dashboard:
- Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
- SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)
7. Supply Chain Analysis Dashboard:
- Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
- Data.gov: [Supply chain datasets](https://www.data.gov/)
8. Healthcare Analytics Dashboard:
- CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
- HealthData.gov: [Healthcare datasets](https://healthdata.gov/)
9. Employee Performance Dashboard:
- Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
- Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)
10. Real-time Dashboard:
- Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
- Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.
1. Sales Performance Dashboard:
- Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
- UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)
2. Customer Segmentation Analysis:
- Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
- UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)
3. Inventory Management Dashboard:
- Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
- Data.gov: [Inventory datasets](https://www.data.gov/)
4. Financial Analysis Dashboard:
- Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
- Quandl: [Financial datasets](https://www.quandl.com/)
5. Social Media Analytics Dashboard:
- Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
- Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)
6. Website Analytics Dashboard:
- Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
- SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)
7. Supply Chain Analysis Dashboard:
- Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
- Data.gov: [Supply chain datasets](https://www.data.gov/)
8. Healthcare Analytics Dashboard:
- CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
- HealthData.gov: [Healthcare datasets](https://healthdata.gov/)
9. Employee Performance Dashboard:
- Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
- Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)
10. Real-time Dashboard:
- Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
- Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.
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