What are predictive algorithms in the context of the stock market?
https://news.1rj.ru/str/stockmarketingfun/277
https://news.1rj.ru/str/stockmarketingfun/277
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Every Data Scientist should know this
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https://www.linkedin.com/posts/sql-analysts_data-science-cheatsheet-activity-7144556047448391680-ir90?utm_source=share&utm_medium=member_android
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https://www.linkedin.com/posts/sql-analysts_data-science-cheatsheet-activity-7144556047448391680-ir90?utm_source=share&utm_medium=member_android
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Mastering Shortcuts for Data Scientists
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https://www.linkedin.com/posts/sql-analysts_mastering-shortcuts-for-data-scientists-activity-7145277074553970688-hVkJ?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_mastering-shortcuts-for-data-scientists-activity-7145277074553970688-hVkJ?utm_source=share&utm_medium=member_android
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To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
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Additional Resources To Assist Research
https://www.reddit.com/r/MachineLearning/
• https://www.reddit.com/r/deeplearning/
• https://paperswithcode.com/
• https://www.datasimplifier.com/
• https://papers.nips.cc/
• https://icml.cc/
• https://iclr.cc/
• https://www.researchgate.net/
ENJOY LEARNING 👍👍
https://www.reddit.com/r/MachineLearning/
• https://www.reddit.com/r/deeplearning/
• https://paperswithcode.com/
• https://www.datasimplifier.com/
• https://papers.nips.cc/
• https://icml.cc/
• https://iclr.cc/
• https://www.researchgate.net/
ENJOY LEARNING 👍👍
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Data Science Interview Questions
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https://www.linkedin.com/posts/sql-analysts_data-science-interview-questions-activity-7151094128284479489-YvbU?utm_source=share&utm_medium=member_android
👇👇
https://www.linkedin.com/posts/sql-analysts_data-science-interview-questions-activity-7151094128284479489-YvbU?utm_source=share&utm_medium=member_android
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All Data Analytics, SQL, Python, ML, Data Science & other useful Study materials complete free Notes😍🔥
https://www.linkedin.com/posts/sql-analysts_all-data-analytics-sql-python-ml-data-activity-7152184466231222272-gEFZ?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_all-data-analytics-sql-python-ml-data-activity-7152184466231222272-gEFZ?utm_source=share&utm_medium=member_android
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Free Resources to learn Data Science 👇👇
https://www.linkedin.com/posts/sql-analysts_sql-notes-activity-7159410174644883456-3VNY?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_sql-notes-activity-7159410174644883456-3VNY?utm_source=share&utm_medium=member_android
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Important Machine Learning Algorithms 👇👇
- 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://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
- 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://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
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Thanks for the amazing response in last post
Here is a simple explanation of each algorithm:
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
Here is a simple explanation of each algorithm:
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
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Data Science & Machine Learning
Thanks for the amazing response in last post Here is a simple explanation of each algorithm: 1. Linear Regression: - Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to…
Deep from Kaggle Group asked me to explain each parameters used in ml algorithms and why we use it in detail.
Like this post if you want next few posts on that topic
Like this post if you want next few posts on that topic
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Data Science & Machine Learning
Deep from Kaggle Group asked me to explain each parameters used in ml algorithms and why we use it in detail. Like this post if you want next few posts on that topic
Amazing response guys!
Let's start with the first algorithm:
1. Linear Regression:
- Parameter:
- None (for basic linear regression): There are no specific hyperparameters for a simple linear regression model.
- Why: Linear regression is a straightforward algorithm where the model fits a line to the data, and there are minimal parameters to tweak. The primary focus is often on the quality of the data and assumptions related to linearity.
Let's start with the first algorithm:
1. Linear Regression:
- Parameter:
- None (for basic linear regression): There are no specific hyperparameters for a simple linear regression model.
- Why: Linear regression is a straightforward algorithm where the model fits a line to the data, and there are minimal parameters to tweak. The primary focus is often on the quality of the data and assumptions related to linearity.
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2. Decision Trees:
- Parameters:
- Max Depth: Limits the depth of the tree by restricting the number of questions it can ask.
- Min Samples Split: Specifies the minimum number of samples required to split a node.
- Min Samples Leaf: Sets the minimum number of samples a leaf node must have.
- Why: These parameters control the complexity of the decision tree. Adjusting them helps prevent overfitting (capturing noise in the data) and ensures a more generalizable model.
- Parameters:
- Max Depth: Limits the depth of the tree by restricting the number of questions it can ask.
- Min Samples Split: Specifies the minimum number of samples required to split a node.
- Min Samples Leaf: Sets the minimum number of samples a leaf node must have.
- Why: These parameters control the complexity of the decision tree. Adjusting them helps prevent overfitting (capturing noise in the data) and ensures a more generalizable model.
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Step by step guide to implement ML algorithms using python 👇
https://www.linkedin.com/posts/sql-analysts_machine-learning-learn-today-activity-7161010726122270721-LLJ0?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_machine-learning-learn-today-activity-7161010726122270721-LLJ0?utm_source=share&utm_medium=member_android
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Python Packages for Data Science in 2024 👇👇
https://www.linkedin.com/posts/sql-analysts_popular-python-packages-for-data-science-activity-7161294412151443457-M3cA?utm_source=share&utm_medium=member_android
https://www.linkedin.com/posts/sql-analysts_popular-python-packages-for-data-science-activity-7161294412151443457-M3cA?utm_source=share&utm_medium=member_android