𝟳+ 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👍5
𝟲 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟 & 𝗠𝗟 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Looking to break into data analytics, data science, or machine learning this year?💻
These 6 free online courses from world-class universities and tech giants like Harvard, Stanford, MIT, Google, and IBM will help you build a job-ready skillset👨💻📌
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Enjoy Learning ✅️
Looking to break into data analytics, data science, or machine learning this year?💻
These 6 free online courses from world-class universities and tech giants like Harvard, Stanford, MIT, Google, and IBM will help you build a job-ready skillset👨💻📌
𝐋𝐢𝐧𝐤👇:-
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Enjoy Learning ✅️
👍1
10 Machine Learning Concepts You Must Know
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias → underfitting; High variance → overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias → underfitting; High variance → overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
👍2
𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗔𝗱𝗱 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Looking to land an internship, secure a tech job, or start freelancing in 2025?👨💻
Python projects are one of the best ways to showcase your skills and stand out in today’s competitive job market🗣📌
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Stand out in today’s competitive job market✅️
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Python projects are one of the best ways to showcase your skills and stand out in today’s competitive job market🗣📌
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Stand out in today’s competitive job market✅️
👍4
NumPy_SciPy_Pandas_Quandl_Cheat_Sheet.pdf
134.6 KB
Cheatsheet on Numpy and pandas for easy viewing 👀
ibm_machine_learning_for_dummies.pdf
1.8 MB
Short Machine Learning guide on industry applications and how it’s used to resolve problems 💡
Real-time sentiment analysis.zip
611 B
FREE PROJECTS WITH SOURCE CODE
1655183344172.pdf
333.8 KB
Algorithmic concepts for anyone who is taking Data Structure and Algorithms, or interested in algorithmic trading 😉
👍3
𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!)😍
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Start Here — With Zero Cost and Maximum Value!💰📌
If you’re aiming for a career in data analytics, now is the perfect time to get started🚀
𝐋𝐢𝐧𝐤👇:-
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A great starting point if you’re brand new to the field✅️
👍1
𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗘𝘃𝗲𝗻 𝗶𝗳 𝗬𝗼𝘂’𝗿𝗲 𝗮 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿!) 📊
Wanna break into data science but feel overwhelmed by too many courses, buzzwords, and conflicting advice? You’re not alone.
Here’s the truth: You don’t need a PhD or 10 certifications. You just need the right skills in the right order.
Let me show you a proven 5-step roadmap that actually works for landing data science roles (even entry-level) 👇
🔹 Step 1: Learn the Core Tools (This is Your Foundation)
Focus on 3 key tools first—don’t overcomplicate:
✅ Python – NumPy, Pandas, Matplotlib, Seaborn
✅ SQL – Joins, Aggregations, Window Functions
✅ Excel – VLOOKUP, Pivot Tables, Data Cleaning
🔹 Step 2: Master Data Cleaning & EDA (Your Real-World Skill)
Real data is messy. Learn how to:
✅ Handle missing data, outliers, and duplicates
✅ Visualize trends using Matplotlib/Seaborn
✅ Use groupby(), merge(), and pivot_table()
🔹 Step 3: Learn ML Basics (No Fancy Math Needed)
Stick to core algorithms first:
✅ Linear & Logistic Regression
✅ Decision Trees & Random Forest
✅ KMeans Clustering + Model Evaluation Metrics
🔹 Step 4: Build Projects That Prove Your Skills
One strong project > 5 courses. Create:
✅ Sales Forecasting using Time Series
✅ Movie Recommendation System
✅ HR Analytics Dashboard using Python + Excel
📍 Upload them on GitHub. Add visuals, write a good README, and share on LinkedIn.
🔹 Step 5: Prep for the Job Hunt (Your Personal Brand Matters)
✅ Create a strong LinkedIn profile with keywords like “Aspiring Data Scientist | Python | SQL | ML”
✅ Add GitHub link + Highlight your Projects
✅ Follow Data Science mentors, engage with content, and network for referrals
🎯 No shortcuts. Just consistent baby steps.
Every pro data scientist once started as a beginner. Stay curious, stay consistent.
Free Data Science Resources: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
ENJOY LEARNING 👍👍
Wanna break into data science but feel overwhelmed by too many courses, buzzwords, and conflicting advice? You’re not alone.
Here’s the truth: You don’t need a PhD or 10 certifications. You just need the right skills in the right order.
Let me show you a proven 5-step roadmap that actually works for landing data science roles (even entry-level) 👇
🔹 Step 1: Learn the Core Tools (This is Your Foundation)
Focus on 3 key tools first—don’t overcomplicate:
✅ Python – NumPy, Pandas, Matplotlib, Seaborn
✅ SQL – Joins, Aggregations, Window Functions
✅ Excel – VLOOKUP, Pivot Tables, Data Cleaning
🔹 Step 2: Master Data Cleaning & EDA (Your Real-World Skill)
Real data is messy. Learn how to:
✅ Handle missing data, outliers, and duplicates
✅ Visualize trends using Matplotlib/Seaborn
✅ Use groupby(), merge(), and pivot_table()
🔹 Step 3: Learn ML Basics (No Fancy Math Needed)
Stick to core algorithms first:
✅ Linear & Logistic Regression
✅ Decision Trees & Random Forest
✅ KMeans Clustering + Model Evaluation Metrics
🔹 Step 4: Build Projects That Prove Your Skills
One strong project > 5 courses. Create:
✅ Sales Forecasting using Time Series
✅ Movie Recommendation System
✅ HR Analytics Dashboard using Python + Excel
📍 Upload them on GitHub. Add visuals, write a good README, and share on LinkedIn.
🔹 Step 5: Prep for the Job Hunt (Your Personal Brand Matters)
✅ Create a strong LinkedIn profile with keywords like “Aspiring Data Scientist | Python | SQL | ML”
✅ Add GitHub link + Highlight your Projects
✅ Follow Data Science mentors, engage with content, and network for referrals
🎯 No shortcuts. Just consistent baby steps.
Every pro data scientist once started as a beginner. Stay curious, stay consistent.
Free Data Science Resources: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
ENJOY LEARNING 👍👍
👍3
Forwarded from Python Projects & Resources
𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨🎓📌
𝐋𝐢𝐧𝐤👇:-
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All at zero cost!🎊✅️
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𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Ready to upskill in data science for free?🚀
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𝐋𝐢𝐧𝐤👇:-
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Take the first step towards your dream career!✅️
Ready to upskill in data science for free?🚀
Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/43GspSO
Take the first step towards your dream career!✅️
Important Django Interview Questions
1. What is the command to install Django and to know about its version?
2. What is the command to create a project and app in Django?
3. What is the command to run a project in Django?
4. What is the command for migrations in Django?
5. What is the Command To Create a Superuser in Django?
6. What is the Django command to view a database schema of an existing (or legacy) database?
7. How to view all items in the Model using Django QuerySet?
8. How to filter items in the Model using Django QuerySet?
9. How to get a particular item in the Model using Django QuerySet?
10. How to delete/insert/update an object using QuerySet in Django?
11. How can you combine multiple QuerySets in a View?
12. Explain Django Architecture? Explain Model, Template, and Views.
13. Explain how a request is processed in Django?
14. What is the difference between a project and an app in Django?
15. Which is the default database in the settings file in Django?
16. Why is Django called a loosely coupled framework?
17. Which is the default port for the Django development server?
18. Explain the Migration in Django.
19. What is Django ORM?
20. Explain how you can set up the Database in Django?
21. What do you mean by the CSRF Token?
22. What is a QuerySet in Django?
23. Difference between select_related and prefetch_related in Django?
24. Difference between Emp.object.filter(), Emp.object.get() and Emp.objects.all() in Django Queryset?
25. Which Companies Use Django?
26. How Static Files are defined in Django? Explain its COnfiguration and uses.
27. What is the difference between Flask, Pyramid, and Django?
28. Give a brief about the Django admin.
29. What databases are supported by Django?
30. What are the advantages/disadvantages of using Django?
31. What is the Django shortcut method to more easily render an HTML response?
32. What is the difference between Authentication and Authorization in Django?
33. What is django.shortcuts.render function?
34. Explain Q objects in Django ORM?
35. What is the significance of the [manage.py] file in Django?
36. What is the use of the include function in the [urls.py] file in Django?
37. What does {% include %} do in Django?
38. What is Django Rest Framework(DRF)?
39. What is a Middleware in Django?
40. What is a session in Django?
41. What are Django Signals?
42. What is the context in Django?
43. What are Django exceptions?
44. What happens if MyObject.objects.get() is called with parameters that do not match an existing item in the database?
45. How to make a variable available to all the templates?
46. Why does Django use regular expressions to define URLs? Is it necessary to use them?
47. Difference between Django OneToOneField and ForeignKey Field?
48. Briefly explain Django Field Class and its types
49. Explain how you can use file-based sessions?
50. What is Jinja templating?
51. What is serialization in Django?
52. What are generic views?
53. What is mixin?
54. Explain the caching strategies in Django?
55. How to get user agent in django
56. What is manager in django model.
57. Why django queries are lazy.
1. What is the command to install Django and to know about its version?
2. What is the command to create a project and app in Django?
3. What is the command to run a project in Django?
4. What is the command for migrations in Django?
5. What is the Command To Create a Superuser in Django?
6. What is the Django command to view a database schema of an existing (or legacy) database?
7. How to view all items in the Model using Django QuerySet?
8. How to filter items in the Model using Django QuerySet?
9. How to get a particular item in the Model using Django QuerySet?
10. How to delete/insert/update an object using QuerySet in Django?
11. How can you combine multiple QuerySets in a View?
12. Explain Django Architecture? Explain Model, Template, and Views.
13. Explain how a request is processed in Django?
14. What is the difference between a project and an app in Django?
15. Which is the default database in the settings file in Django?
16. Why is Django called a loosely coupled framework?
17. Which is the default port for the Django development server?
18. Explain the Migration in Django.
19. What is Django ORM?
20. Explain how you can set up the Database in Django?
21. What do you mean by the CSRF Token?
22. What is a QuerySet in Django?
23. Difference between select_related and prefetch_related in Django?
24. Difference between Emp.object.filter(), Emp.object.get() and Emp.objects.all() in Django Queryset?
25. Which Companies Use Django?
26. How Static Files are defined in Django? Explain its COnfiguration and uses.
27. What is the difference between Flask, Pyramid, and Django?
28. Give a brief about the Django admin.
29. What databases are supported by Django?
30. What are the advantages/disadvantages of using Django?
31. What is the Django shortcut method to more easily render an HTML response?
32. What is the difference between Authentication and Authorization in Django?
33. What is django.shortcuts.render function?
34. Explain Q objects in Django ORM?
35. What is the significance of the [manage.py] file in Django?
36. What is the use of the include function in the [urls.py] file in Django?
37. What does {% include %} do in Django?
38. What is Django Rest Framework(DRF)?
39. What is a Middleware in Django?
40. What is a session in Django?
41. What are Django Signals?
42. What is the context in Django?
43. What are Django exceptions?
44. What happens if MyObject.objects.get() is called with parameters that do not match an existing item in the database?
45. How to make a variable available to all the templates?
46. Why does Django use regular expressions to define URLs? Is it necessary to use them?
47. Difference between Django OneToOneField and ForeignKey Field?
48. Briefly explain Django Field Class and its types
49. Explain how you can use file-based sessions?
50. What is Jinja templating?
51. What is serialization in Django?
52. What are generic views?
53. What is mixin?
54. Explain the caching strategies in Django?
55. How to get user agent in django
56. What is manager in django model.
57. Why django queries are lazy.
👍3
Forwarded from Artificial Intelligence
𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GZZUXi
All at zero cost!🎊✅️
Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GZZUXi
All at zero cost!🎊✅️
If you're a data science beginner, Python is the best programming language to get started.
Here are 7 Python libraries for data science you need to know if you want to learn:
- Data analysis
- Data visualization
- Machine learning
- Deep learning
NumPy
NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
Pandas
Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.
Matplotlib
Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.
Scikit-learn
Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.
Seaborn
Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.
TensorFlow or PyTorch
TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.
SciPy
Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.
Enjoy 😄👍
Here are 7 Python libraries for data science you need to know if you want to learn:
- Data analysis
- Data visualization
- Machine learning
- Deep learning
NumPy
NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
Pandas
Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.
Matplotlib
Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.
Scikit-learn
Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.
Seaborn
Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.
TensorFlow or PyTorch
TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.
SciPy
Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.
Enjoy 😄👍
👍3
Forwarded from Python Projects & Resources
𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲𝘀 – 𝗙𝗿𝗲𝗲 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗚𝘂𝗶𝗱𝗲😍
If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jg88I8
All The Best 🎊
If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jg88I8
All The Best 🎊
👍2
𝗛𝗼𝘄 𝘁𝗼 𝗚𝗲𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱 𝗶𝗻 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗭𝗲𝗿𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲!🧠⚡
AI might sound complex. But guess what?
You don’t need a PhD or 5 years of experience to break into this field.
Here’s your 6-step beginner roadmap to launch your AI journey the smart way👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics of Python (Your AI Superpower)
Python is the language of AI.
✅ Learn variables, loops, functions, and data structures
✅ Practice with platforms like W3Schools, SoloLearn, or Replit
✅ Understand NumPy & Pandas basics (they’ll be your go-to tools)
🔹 𝗦𝘁𝗲𝗽 𝟮: Understand What AI Really Is
Before diving deep, get clarity.
✅ What is AI vs ML vs Deep Learning?
✅ Learn core concepts like Supervised vs Unsupervised Learning
✅ Follow beginner-friendly YouTubers like “StatQuest” or “Codebasics”
🔹 𝗦𝘁𝗲𝗽 𝟯: Build Simple AI Projects (Even as a Beginner)
Start applying your skills with fun mini-projects:
✅ Spam Email Classifier
✅ House Price Predictor
✅ Rock-Paper-Scissors Game using AI
Pro Tip: Use scikit-learn for most of these!
🔹 𝗦𝘁𝗲𝗽 𝟰: Get Comfortable with Data (AI Runs on It!)
AI = Algorithms + Data
✅ Learn basic data cleaning with Pandas
✅ Explore simple datasets from Kaggle or UCI ML Repository
✅ Practice EDA (Exploratory Data Analysis) with Matplotlib & Seaborn
🔹 𝗦𝘁𝗲𝗽 𝟱: Take Free AI Courses (No Cost Learning)
You don’t need a fancy bootcamp to start learning.
✅ “AI For Everyone” by Andrew Ng (Coursera)
✅ “Machine Learning with Python” by IBM (edX)
✅ Kaggle’s Learn Track: Intro to ML
🔹 𝗦𝘁𝗲𝗽 𝟲: Join AI Communities & Share Your Work
✅ Join AI Discord servers, Reddit threads, and LinkedIn groups
✅ Post your projects on GitHub
✅ Engage in AI hackathons, challenges, and build in public
Your network = Your next opportunity.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 = 𝗬𝗼𝘂𝗿 𝗘𝗻𝘁𝗿𝘆 𝗣𝗼𝗶𝗻𝘁
It’s not about knowing everything—it’s about starting.
Consistency will compound.
You’ll go from “beginner” to “builder” faster than you think.
Free Artificial Intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
#ai
AI might sound complex. But guess what?
You don’t need a PhD or 5 years of experience to break into this field.
Here’s your 6-step beginner roadmap to launch your AI journey the smart way👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics of Python (Your AI Superpower)
Python is the language of AI.
✅ Learn variables, loops, functions, and data structures
✅ Practice with platforms like W3Schools, SoloLearn, or Replit
✅ Understand NumPy & Pandas basics (they’ll be your go-to tools)
🔹 𝗦𝘁𝗲𝗽 𝟮: Understand What AI Really Is
Before diving deep, get clarity.
✅ What is AI vs ML vs Deep Learning?
✅ Learn core concepts like Supervised vs Unsupervised Learning
✅ Follow beginner-friendly YouTubers like “StatQuest” or “Codebasics”
🔹 𝗦𝘁𝗲𝗽 𝟯: Build Simple AI Projects (Even as a Beginner)
Start applying your skills with fun mini-projects:
✅ Spam Email Classifier
✅ House Price Predictor
✅ Rock-Paper-Scissors Game using AI
Pro Tip: Use scikit-learn for most of these!
🔹 𝗦𝘁𝗲𝗽 𝟰: Get Comfortable with Data (AI Runs on It!)
AI = Algorithms + Data
✅ Learn basic data cleaning with Pandas
✅ Explore simple datasets from Kaggle or UCI ML Repository
✅ Practice EDA (Exploratory Data Analysis) with Matplotlib & Seaborn
🔹 𝗦𝘁𝗲𝗽 𝟱: Take Free AI Courses (No Cost Learning)
You don’t need a fancy bootcamp to start learning.
✅ “AI For Everyone” by Andrew Ng (Coursera)
✅ “Machine Learning with Python” by IBM (edX)
✅ Kaggle’s Learn Track: Intro to ML
🔹 𝗦𝘁𝗲𝗽 𝟲: Join AI Communities & Share Your Work
✅ Join AI Discord servers, Reddit threads, and LinkedIn groups
✅ Post your projects on GitHub
✅ Engage in AI hackathons, challenges, and build in public
Your network = Your next opportunity.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 = 𝗬𝗼𝘂𝗿 𝗘𝗻𝘁𝗿𝘆 𝗣𝗼𝗶𝗻𝘁
It’s not about knowing everything—it’s about starting.
Consistency will compound.
You’ll go from “beginner” to “builder” faster than you think.
Free Artificial Intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
#ai
👍5
Forwarded from Artificial Intelligence
𝟯 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍
👩💻 Want to Break into Data Science but Don’t Know Where to Start?🚀
The best way to begin your data science journey is with hands-on projects using real-world datasets.👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44LoViW
Enjoy Learning ✅️
👩💻 Want to Break into Data Science but Don’t Know Where to Start?🚀
The best way to begin your data science journey is with hands-on projects using real-world datasets.👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44LoViW
Enjoy Learning ✅️
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://news.1rj.ru/str/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://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
👍4
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 🎓️
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 🎓️
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