𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

If you’re job hunting, switching careers, or just want to upgrade your skill set — Google Skillshop is your go-to platform in 2025!

Google offers completely free certifications that are globally recognized and valued by employers in tech, digital marketing, business, and analytics📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4dwlDT2

Enroll For FREE & Get Certified 🎓️

Core machine learning concepts explained through memes and simple charts created by Mihail Eric.

🔍 Machine Learning Cheat Sheet 🔍

1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.

2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)

3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.

4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.

5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.

6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.

7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.

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

All the best 👍👍

𝟳 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗖𝗼𝘀𝘁, 𝗡𝗼 𝗖𝗮𝘁𝗰𝗵!)😍

Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌

From Python and machine learning to hands-on projects and challenges🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4dAuymr

Enjoy Learning ✅️

🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://t.iss.one/pythondevelopersindia/76

❯ https://youtu.be/rfscVS0vtbw?si=WdvcwfYR3PaLiyJQ

3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python

4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/

5. Take it to next level

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Machine Learning
https://developers.google.com/machine-learning/crash-course

https://t.iss.one/datasciencefun/290

❯ Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning

Please more reaction with our posts

Credits: https://t.iss.one/datasciencefree

𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍

If you’re serious about AI, you can’t skip Deep Learning—and this FREE course from MIT is one of the best ways to start👨‍💻📌

Offered by MIT’s top researchers and engineers, this online course is open to everyone, no matter where you live or work🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3H6cggR

Why wait to get started when you can learn from MIT for free?✅️

🚦Top 10 Data Science Tools🚦

Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.

🛰What is Data Science ?

Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .

🗽Top Data Science Tools that are normally utilized :

1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .

2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.

3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.

4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.

5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.

6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.

7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.

8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.

9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.

10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Data Analytics :- https://pdlink.in/3Fq7E4p

Data Science :- https://pdlink.in/4iSWjaP

SQL :- https://pdlink.in/3EyjUPt

Python :- https://pdlink.in/4c7hGDL

Web Dev :- https://bit.ly/4ffFnJZ

AI :- https://pdlink.in/4d0SrTG

Enroll For FREE & Get Certified 🎓

Theoretical Questions for Coding Interviews on Basic Data Structures

1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.

2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.

3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.

4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).

5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).

6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.

7. What is the difference between an array and a linked list?

Array: Fixed size, elements stored in contiguous memory.

Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.


8. What is the time complexity for accessing an element in an array vs. a linked list?

Array: O(1) for direct access by index.

Linked List: O(n) for access, as you must traverse the list from the start to find an element.


9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?

Array:

Insertion/Deletion at the end: O(1).

Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.


Linked List:

Insertion/Deletion at the beginning: O(1).

Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.



10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).

Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

𝟰 𝗙𝗿𝗲𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗗𝗮𝗶𝗹𝘆 (𝗡𝗼 𝗦𝗶𝗴𝗻𝘂𝗽 𝗡𝗲𝗲𝗱𝗲𝗱!)😍

🚀 Want to Sharpen Your Data Analytics Skills for FREE?💫

If you’re learning data analytics and want to build real skills, theory alone won’t cut it. You need hands-on practice—and the best part? You can do it daily, for free!🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44WK6ie

Enjoy Learning ✅️