Machine Learning with Decision Trees and Random Forest 📝.pdf
1.8 MB
Machine Learning with Decision Trees and Random Forest 📝.pdf
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Data Scientist:
Focuses on data cleaning, preprocessing, and exploratory data analysis (EDA).
Utilizes statistical modeling, hypothesis testing, and machine learning model development.
AI Engineer: - Specializes in model deployment, integration, and optimizing model performance.
Focuses on data cleaning, preprocessing, and exploratory data analysis (EDA).
Utilizes statistical modeling, hypothesis testing, and machine learning model development.
AI Engineer: - Specializes in model deployment, integration, and optimizing model performance.
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Top 5 key developments happening today in the AI and tech space.
1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.
2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.
3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.
4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.
5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.
1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.
2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.
3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.
4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.
5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.
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AI as a life saver:
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review
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Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
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How to learn Artificial Intelligence from scratch
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https://medium.com/@data_analyst/how-to-learn-artificial-intelligence-from-scratch-d34ea18f70c1?sk=b139911f85a0d0c0ecd448a7fffe4c9d
👇👇
https://medium.com/@data_analyst/how-to-learn-artificial-intelligence-from-scratch-d34ea18f70c1?sk=b139911f85a0d0c0ecd448a7fffe4c9d
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5 Algorithms you must know as a data scientist 👩💻 🧑💻
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/free4unow_backup
Like if you need similar content 😄👍
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/free4unow_backup
Like if you need similar content 😄👍
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Forwarded from Crypto Trends
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Introducing ChatGPT search
ChatGPT can now search the web in a much better way than before. You can get fast, timely answers with links to relevant web sources, which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more.
ChatGPT will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.
On mobile, the option will replace the existing “Refine my draft” shortcut. Instead of swiping to see options to polish.
Search will be available at chatgpt.com(opens in a new window), as well as on our desktop and mobile apps. All ChatGPT Plus and Team users, as well as SearchGPT waitlist users, will have access today. Enterprise and Edu users will get access in the next few weeks. We’ll roll out to all Free users over the coming months.
ChatGPT can now search the web in a much better way than before. You can get fast, timely answers with links to relevant web sources, which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more.
ChatGPT will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.
On mobile, the option will replace the existing “Refine my draft” shortcut. Instead of swiping to see options to polish.
Search will be available at chatgpt.com(opens in a new window), as well as on our desktop and mobile apps. All ChatGPT Plus and Team users, as well as SearchGPT waitlist users, will have access today. Enterprise and Edu users will get access in the next few weeks. We’ll roll out to all Free users over the coming months.
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85 career-focused Data and AI courses for FREE until Nov 21st
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https://365datascience.pxf.io/BnE1P4
No credit card required
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https://365datascience.pxf.io/BnE1P4
No credit card required
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Google could add AI replies to its handy call-screening feature
Google could soon add “AI Replies” to the Phone app’s call-screening feature. A line of code spotted by 9to5Google suggests the app will generate “new AI-powered smart replies” based on how someone responds to the call screen.
Google widely rolled out its call-screening feature in Android 12. It allows you to filter calls and have Google Assistant respond with an audio message to ask who’s calling, rather than having to pick up the call yourself. Late last year, Google added “contextual replies,” which use the context of someone’s call to serve up customized audio responses. It also updated its call-screening feature in March with a way to respond even when the caller is silent.
Source-Link: The Verge
Google could soon add “AI Replies” to the Phone app’s call-screening feature. A line of code spotted by 9to5Google suggests the app will generate “new AI-powered smart replies” based on how someone responds to the call screen.
Google widely rolled out its call-screening feature in Android 12. It allows you to filter calls and have Google Assistant respond with an audio message to ask who’s calling, rather than having to pick up the call yourself. Late last year, Google added “contextual replies,” which use the context of someone’s call to serve up customized audio responses. It also updated its call-screening feature in March with a way to respond even when the caller is silent.
Source-Link: The Verge
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Time to have an uncomfortable conversation 😬
📊 67.6% of developers admire Python. 58.3% admire JavaScript. That's almost a 10-point difference in favor of Python.
💡39.8% want to learn JavaScript, but 41.9% want to learn Python. That's a 2+ difference.
🚀 TypeScript doesn't do better. Only 33.8% want to learn TypeScript, although it's more admired than Python, with 69.5%. 🤷♂️
📖 This data is from the 2024 Stack Overflow Survey. 📋
🌍 On top of that, Python has surpassed JavaScript as the most popular programming language on GitHub this year.
There's a clear trend here. 📈
This is the first chapter of what will become a complete Python dominance (likely thanks to the rise of AI). 🤖✨
📊 67.6% of developers admire Python. 58.3% admire JavaScript. That's almost a 10-point difference in favor of Python.
💡39.8% want to learn JavaScript, but 41.9% want to learn Python. That's a 2+ difference.
🚀 TypeScript doesn't do better. Only 33.8% want to learn TypeScript, although it's more admired than Python, with 69.5%. 🤷♂️
📖 This data is from the 2024 Stack Overflow Survey. 📋
🌍 On top of that, Python has surpassed JavaScript as the most popular programming language on GitHub this year.
There's a clear trend here. 📈
This is the first chapter of what will become a complete Python dominance (likely thanks to the rise of AI). 🤖✨
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This message is for all those people looking for new opportunities or learning new skills thinking if they'll earn more, sustain in this life or not.
AI will take the job.
Will there be new opportunities in 2024.
How many days will it take to learn this skill.
Why I am still not successful.
I am sharing some bit of experience with you all based on whatever I observed in this world.
Don't think too much. Everything takes some time.
Rather just focus on your goal and do something which keep you closer to that. Stay consistent & work on something that your future self will be proud of.
There will be some days when you'll find yourself doing nothing. But just ignore it and learn from the failures without thinking anything negative.
In case I can be of any help to you, feel free to reach out to me either through Instagram or Telegram.
Never stop learning ❤️
Learning can be anything - new skill or habit. So just enjoy the process even if it takes time.
ENJOY LEARNING 👍👍
AI will take the job.
Will there be new opportunities in 2024.
How many days will it take to learn this skill.
Why I am still not successful.
I am sharing some bit of experience with you all based on whatever I observed in this world.
Don't think too much. Everything takes some time.
Rather just focus on your goal and do something which keep you closer to that. Stay consistent & work on something that your future self will be proud of.
There will be some days when you'll find yourself doing nothing. But just ignore it and learn from the failures without thinking anything negative.
In case I can be of any help to you, feel free to reach out to me either through Instagram or Telegram.
Never stop learning ❤️
Learning can be anything - new skill or habit. So just enjoy the process even if it takes time.
ENJOY LEARNING 👍👍
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