6 essential Python functions for file handling:

🔹 open(): Opens a file and returns a file object. Essential for reading and writing files

🔹 read(): Reads the contents of a file

🔹 write(): Writes data to a file. Great for saving output

🔹 close(): Closes the file

🔹 with open(): Context manager for file operations. Ensures proper file handling

🔹 pd.read_excel(): Reads Excel files into a pandas DataFrame. Crucial for working with Excel data

What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀?

https://www.linkedin.com/posts/sql-analysts_what-%3F%3F-%3F%3F%3F%3F%3F%3F%3F%3F-are-commonly-asked-activity-7228986128274493441-ZIyD

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Support Vector Machines clearly explained👇


1. Support Vector Machine is a useful Machine Learning algorithm frequently used for both classification and regression problems.

⭐ this is a 𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺.

Basically, they need labels or targets to learn!

2. Its goal is to find a boundary that maximally separates the data into different classes (classification) or fits the data with a line/plane (regression).

They excel at handling intricate datasets where finding the right boundary seems challenging.

3. For data with non-linear relationships, finding a boundary is impossible. This boundary is called 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗻𝗴 𝗵𝘆𝗽𝗲𝗿𝗽𝗹𝗮𝗻𝗲.

The points closest to this boundary, named 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘃𝗲𝗰𝘁𝗼𝗿𝘀, play a key role in shaping the SVM’s decision-making process.

4. But let’s go back to finding the boundaries...

To overcome linear limitations, SVMs take the data and project it into a higher-dimensional space, where finding the boundary becomes much easier.

This boundary is called the maximum margin hyperplane.

5. To transform the data to a higher-dimensional space, SVMs use what is called 𝗸𝗲𝗿𝗻𝗲𝗹 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀.

There are two main types:
1️⃣ Polynomial kernels
2️⃣ Radial kernels

6. 🟢 𝗔𝗗𝗩𝗔𝗡𝗧𝗔𝗚𝗘𝗦 🟢

• useful when the data is not linearly separable

• very effective in high-dimensional data and can handle a large number of features with relatively small datasets

7. 🔴 𝗗𝗜𝗦𝗔𝗗𝗩𝗔𝗡𝗧𝗔𝗚𝗘𝗦 🔴

• Sensitive to the choice of kernel function

• Sensitive to the choice of regularization parameter, which determines the trade-off between finding a good boundary and avoiding overfitting.

Common Python errors and what they mean:

🔹 SyntaxError: Incorrectly written code structure. Check for typos or missing punctuation (like missing '';,).

🔹 IndentationError: Inconsistent use of spaces and tabs. Keep your indentation consistent.

🔹 TypeError: Performing an operation on incompatible types. Like adding a string and an integer ⤵️
🔹 NameError: Using a variable or function that hasn't been defined. Like print(undeclared_variable)

🔹 ValueError: Function receives the correct type but an inappropriate value. When you are trying to convert str to ing, like int("abc")

How to choose your data science career 👇👇
https://www.linkedin.com/posts/sql-analysts_best-courses-on-data-science-ai-1-data-activity-7229345999612239872-NRcf?utm_source=share&utm_medium=member_android

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Data Analyst vs. Data Scientist 👇👇
https://t.me/sqlspecialist/775

Guesstimate questions are scary, simply because they really matter for impacting your performance in those all-important interviews — often for consulting, data analytics or product management. No need to worry; you can do it! In this guide, we are looking at how to approach guesstimate questions with confidence and make what sounds like a guessing game into an opportunity for showcasing our analytical thinking
👇👇
https://datasimplifier.com/guesstimate-questions/

5 Python functions for statistical analysis:

🔹 mean(): Calculates the average of your data. Perfect for understanding central tendencies.

🔹 median(): Finds the middle value in your data. Useful when your data has outliers.

🔹 mode(): Identifies the most frequent value. Key for categorical data analysis.

🔹 std(): Computes the standard deviation. Crucial for measuring data dispersion.

🔹 var(): Calculates the variance. Helps in understanding data variability. DataAnalytics

Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌

I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

How to enter into Data Science

👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.

👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.

👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.

Struggle of a data scientist