Today's question: Which field can't be replaced by Generative AI?
Tricky question but everyone can have their own opinions 😄
Tricky question but everyone can have their own opinions 😄
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2023 Reflection & 2024 Preview .pdf_20240127_182029_0000.pdf
903 KB
2023 Yearly Reflection & 2024 Preview
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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://t.iss.one/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://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
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Forwarded from 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 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://t.iss.one/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://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
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Forwarded from Startup & Business Ideas
It's productive to not execute the idea that just came to your mind and sparked your great interest immediately.
—-
Years ago, when I haven't build a lot of solutions yet, I usually started to almost instantly create what came to my mind because I was driven by a huge energy of my fascination about problems I may solve. Also, I enjoyed building solutions with code.
Of course, I didn't finish many such things I've started to create because:
- the idea wasn't mesmerizing enough to continue building a solution. Coding 4 hours vs. coding a month is a significant difference.
- I understood that the idea didn't solve problems.
- I found out I'm interested in building other ideas.
And other reasons but they all come down to the absence of reasonable planning. We have limited energy and time, so it's more productive to allocate them to the projects that make sense in the long-term.
Of course, this statement is obvious but I fall into executing not properly planned projects sometimes.
Join for more: https://t.iss.one/Learn_Startup
—-
Years ago, when I haven't build a lot of solutions yet, I usually started to almost instantly create what came to my mind because I was driven by a huge energy of my fascination about problems I may solve. Also, I enjoyed building solutions with code.
Of course, I didn't finish many such things I've started to create because:
- the idea wasn't mesmerizing enough to continue building a solution. Coding 4 hours vs. coding a month is a significant difference.
- I understood that the idea didn't solve problems.
- I found out I'm interested in building other ideas.
And other reasons but they all come down to the absence of reasonable planning. We have limited energy and time, so it's more productive to allocate them to the projects that make sense in the long-term.
Of course, this statement is obvious but I fall into executing not properly planned projects sometimes.
Join for more: https://t.iss.one/Learn_Startup
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Today's question
Which tool do you use for data visualisation?
Reply in comments 👇👇
Which tool do you use for data visualisation?
Reply in comments 👇👇
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Data Science Projects
Today's question Which tool do you use for data visualisation? Reply in comments 👇👇
For those who can't comment, you need to join the group 👇
https://t.iss.one/Kaggle_Group
https://t.iss.one/Kaggle_Group
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Which python library is not specifically used for data visualisation?
Anonymous Poll
21%
Plotly
12%
Seaborn
14%
Matplotlib
53%
Scikit-learn
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Which of the following is not an aggregate function in sql?
Anonymous Quiz
60%
MEAN()
12%
SUM()
19%
MIN()
9%
AVG()
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