🌟Today’s Highlights!** 🌟
Hey friends! 🚀 Another day, another step forward in my Python + Machine Learning adventure. Here’s what I explored today—and let me tell you, it was as exciting as it was challenging!
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🔹 **Arithmetic Operations & Dot Products** 🔢
- Got my hands dirty with matrix arithmetic**—adding, subtracting, and multiplying like a math wizard.(already forgot APPLIED 1😁)
- Took it a notch higher with **dot products, and let’s just say… vectors have never been cooler! 🎯
🔹 **Sorting Like a Pro** 🗂
- Experimented with sorting arrays efficiently and gained some cool insights into how to arrange data seamlessly!
🔹 **Extracting Data from Images as Arrays** 🖼➡️📊
- This part was absolutely mind-blowing!
- I learned how to treat **images as Numpy arrays**, exploring their data like a detective. 🕵️♂️
- It’s amazing to think how ML models “see” images and work with their numerical data.
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🔥 **Today’s Takeaway**: I’m slowly starting to see how these smaller building blocks come together for big-picture machine learning applications. It feels like assembling a puzzle—and each piece makes me more eager to see the final masterpiece!
Got any resources, tips, or fun challenges I should try? Let me know below! 👇
#PythonJourney #MachineLearning #NumPy #ImageProcessing #CodingLife #NeverStopLearning
Hey friends! 🚀 Another day, another step forward in my Python + Machine Learning adventure. Here’s what I explored today—and let me tell you, it was as exciting as it was challenging!
---
🔹 **Arithmetic Operations & Dot Products** 🔢
- Got my hands dirty with matrix arithmetic**—adding, subtracting, and multiplying like a math wizard.(already forgot APPLIED 1😁)
- Took it a notch higher with **dot products, and let’s just say… vectors have never been cooler! 🎯
🔹 **Sorting Like a Pro** 🗂
- Experimented with sorting arrays efficiently and gained some cool insights into how to arrange data seamlessly!
🔹 **Extracting Data from Images as Arrays** 🖼➡️📊
- This part was absolutely mind-blowing!
- I learned how to treat **images as Numpy arrays**, exploring their data like a detective. 🕵️♂️
- It’s amazing to think how ML models “see” images and work with their numerical data.
---
🔥 **Today’s Takeaway**: I’m slowly starting to see how these smaller building blocks come together for big-picture machine learning applications. It feels like assembling a puzzle—and each piece makes me more eager to see the final masterpiece!
Got any resources, tips, or fun challenges I should try? Let me know below! 👇
#PythonJourney #MachineLearning #NumPy #ImageProcessing #CodingLife #NeverStopLearning
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Mike's ML Forge
https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/numpy-exercises.ipynb
here some exercises to tackle Numpy basics
Chinese AI 'DeepSeek' has shaken the global tech industry! Its latest model, DeepSeek-V3, reportedly outperforms GPT-4 and Llama 3 while being trained for just $5.6M—a fraction of competitors' budgets. This disruptive innovation led to a $2 trillion wipeout in U.S. stock markets in a single day, with Nvidia suffering a historic $600B loss. Is this China's 'Sputnik moment' in AI dominance?🤯
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And the V3 model was trained for just $5.6 million—significantly less than competitors like OpenAI, which spent over $100 million to train GPT-4.🤯
Media is too big
VIEW IN TELEGRAM
🔅 Trump calls China's DeepSeek AI a "wake-up call"
The sudden rise of a Chinese startup called DeepSeek sent U.S. tech stocks tumbling Monday. DeepSeek says it created an artificial intelligence model in much less time and for much less money than U.S. companies. President Trump called it a "wake-up call."
🚀 **Diving into Pandas: Data Mastery Begins! 🐼📊
Today, I took a deep dive into **Pandas, one of the most powerful Python libraries for data manipulation! Here’s what I explored:
🔹 **Anatomy of DataFrames – Understanding the core structure of rows, columns, and indexes.
🔹 Series & DataFrames – The building blocks of Pandas for handling structured data.
🔹 Working with CSVs – Importing, exporting, and managing datasets with ease.
🔹 Denoscriptive Statistics – Summarizing data using `.describe()`, `.info()`, and other key methods.
🔹 Selecting & Viewing Data – Filtering rows, slicing columns, and accessing data efficiently.
🔹 Manipulating Data** – Adding, removing, and transforming data like a pro!
Pandas is the backbone of data science and machine learning, and I’m just getting started. Let’s keep pushing forward! 💡📈
#Python #Pandas #DataScience #LearningJourney
Today, I took a deep dive into **Pandas, one of the most powerful Python libraries for data manipulation! Here’s what I explored:
🔹 **Anatomy of DataFrames – Understanding the core structure of rows, columns, and indexes.
🔹 Series & DataFrames – The building blocks of Pandas for handling structured data.
🔹 Working with CSVs – Importing, exporting, and managing datasets with ease.
🔹 Denoscriptive Statistics – Summarizing data using `.describe()`, `.info()`, and other key methods.
🔹 Selecting & Viewing Data – Filtering rows, slicing columns, and accessing data efficiently.
🔹 Manipulating Data** – Adding, removing, and transforming data like a pro!
Pandas is the backbone of data science and machine learning, and I’m just getting started. Let’s keep pushing forward! 💡📈
#Python #Pandas #DataScience #LearningJourney
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some of the main functions from todays lesson
🔹 `regex()` & `str.replace()` – Cleaning messy text data using pattern matching. 🔍✨
🔹 `crosstab()`– Summarizing relationships between categorical variables like a pro. 📊
🔹 `fillna()`– Handling missing data by filling in gaps with smart defaults. 🔄
🔹 'dropna()`– Removing incomplete data to keep things clean and accurate. 🧹
With these tools, data manipulation is becoming second nature! On to the next challenge. 🚀💡
#Python #Pandas #DataScience #DataCleaning
🔹 `regex()` & `str.replace()` – Cleaning messy text data using pattern matching. 🔍✨
🔹 `crosstab()`– Summarizing relationships between categorical variables like a pro. 📊
🔹 `fillna()`– Handling missing data by filling in gaps with smart defaults. 🔄
🔹 'dropna()`– Removing incomplete data to keep things clean and accurate. 🧹
With these tools, data manipulation is becoming second nature! On to the next challenge. 🚀💡
#Python #Pandas #DataScience #DataCleaning
Which python library is not used specifically for data visualization?
Anonymous Quiz
8%
Matplotlib
17%
Plotly
58%
Numpy
17%
Seaborn
been doin some exercises to figure out some of Pandas functions and tbh its fun also a lil bit confusing 🙂
🔥 3 Days of Matplotlib Mastery! 🎨📊
The past three days have been a deep dive into Matplotlib, and wow—visualizing data has never felt this smooth! From basic plots to working with pandas DataFrames and NumPy arrays, here’s what I’ve unlocked:
🔹 Line Plots – Perfect for tracking trends over time.
🔹 Scatter Plots – Ideal for revealing relationships between variables.
🔹 Bar Charts – The go-to for categorical comparisons.
🔹 Histograms – Great for understanding data distributions.
🔹 Subplots – Because one plot is never enough!
💡 I also explored two ways to create subplots:
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✅
The Matplotlib workflow now makes total sense:
1️⃣ Import Matplotlib & NumPy
2️⃣ Load or generate data
3️⃣ Pick the right plot & customize
4️⃣ Display or save the figure
I’m starting to see why data visualization is so powerful! 📊🔥 Let me know if you want to see some of the code examples I worked on. 🚀
#Matplotlib #DataScience
The past three days have been a deep dive into Matplotlib, and wow—visualizing data has never felt this smooth! From basic plots to working with pandas DataFrames and NumPy arrays, here’s what I’ve unlocked:
🔹 Line Plots – Perfect for tracking trends over time.
🔹 Scatter Plots – Ideal for revealing relationships between variables.
🔹 Bar Charts – The go-to for categorical comparisons.
🔹 Histograms – Great for understanding data distributions.
🔹 Subplots – Because one plot is never enough!
💡 I also explored two ways to create subplots:
✅
plt.subplot() – Quick & simple. ✅
plt.subplots() – Gives more control over layout. The Matplotlib workflow now makes total sense:
1️⃣ Import Matplotlib & NumPy
2️⃣ Load or generate data
3️⃣ Pick the right plot & customize
4️⃣ Display or save the figure
I’m starting to see why data visualization is so powerful! 📊🔥 Let me know if you want to see some of the code examples I worked on. 🚀
#Matplotlib #DataScience
Next Chapter: Data Science Foundations! 📊
The next few weeks will be all about pandas, Matplotlib, and NumPy**—the powerhouse trio for data analysis and visualization in Python! 🔥
💡 **Goals:
✅ Master data manipulation with pandas 🏗
✅ Get comfortable with numerical operations using NumPy
✅ Bring data to life with Matplotlib 🎨
To make things even more exciting, I’ll be working on small projects with real-world data—because the best way to learn is by doing! 💪
If you have any cool project ideas or datasets I should explore, Let’s build something awesome. 🚀
#Python #DataScience #Pandas #NumPy #Matplotlib #LearningByDoing
The next few weeks will be all about pandas, Matplotlib, and NumPy**—the powerhouse trio for data analysis and visualization in Python! 🔥
💡 **Goals:
✅ Master data manipulation with pandas 🏗
✅ Get comfortable with numerical operations using NumPy
✅ Bring data to life with Matplotlib 🎨
To make things even more exciting, I’ll be working on small projects with real-world data—because the best way to learn is by doing! 💪
If you have any cool project ideas or datasets I should explore, Let’s build something awesome. 🚀
#Python #DataScience #Pandas #NumPy #Matplotlib #LearningByDoing
Basics of Machine Learning 👇👇
1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.