Data Science & Machine Learning Free Resources
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“The learning community for Data Science, Machine Learning and AI 🤖
1) Data Science
2) Machine Learning
3) Data viz
4) Artificial Intelligence
5) Python
6) Ebooks
7) Articles
8) Free Courses”
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Kanal postlari
| 2 | ✅ Must-Know AI Tools & Platforms (Beginner to Pro) 🤖🛠️
🔹 For Machine Learning & Data Science
• TensorFlow – Google’s open-source ML library for deep learning
• PyTorch – Flexible & beginner-friendly deep learning framework
• Scikit-learn – Best for classic ML (classification, regression, clustering)
• Keras – High-level API to build neural networks fast
🔹 For Natural Language Processing (NLP)
• Hugging Face Transformers – Pretrained models for text, chatbots, translation
• spaCy – Fast NLP for entity recognition & parsing
• NLTK – Basics like tokenization & sentiment analysis
🔹 For Computer Vision
• OpenCV – Image processing & object detection
• YOLO – Real-time object detection
• MediaPipe – Face & hand tracking made easy
🔹 For Generative AI
• Chat / -4 – Text generation, coding, brainstorming
• DALL·E, Midjourney – AI-generated images & art
• Runway ML – AI video editing & creativity tools
🔹 For Robotics & Automation
• ROS – Framework to build robot software
• UiPath, Automation Anywhere – Automate repetitive tasks
🔹 For MLOps & Deployment
• Docker – Package & deploy AI apps
• Kubernetes – Scale models in production
• MLflow – Track & manage ML experiments
💡 Tip: Start small—pick one category, build a mini-project & share it online!
👍 Tap ❤️ if you found this helpful! | 686 |
| 3 |  🖥 Types of Machine Learning | 1 227 |
| 4 | 🎯 Top free Data Science resources 🧠
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
React 🔥 for more | 1 900 |
| 5 |  🔰 What is linear search?
Linear search is a simple searching algorithm that sequentially checks each element in a list or array until a match is found or the end of the list is reached.
It starts from the beginning of the list and compares each element with the target value until it finds a match or exhausts the list.
If the target is found, it returns the index of the target element; otherwise, it returns a special value (often -1) to indicate that the target is not present in the list. | 2 573 |
| 6 | 5 Handy Tips to Master Data Science ⬇️
1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience. | 2 457 |
| 7 | AI & Data Scientist Roadmap ✌️
Don't forget to like ❤️ and share with friends so they can benefit too—completely free!
Thank You! | 2 057 |
| 8 | 91. What is model deployment?
92. What is batch vs real-time prediction?
93. What is model drift?
94. How do you monitor model performance?
95. What is feature store?
96. What is experiment tracking?
97. How do you explain model predictions?
98. What is data versioning?
99. How do you handle failed models?
100. How do you communicate results to non-technical stakeholders?
Double Tap ♥️ For Detailed Answers | 1 941 |
| 9 | Top 100 Data Science Interview Questions ✅
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics?
Deployment and Real-World Practice | 2 107 |
| 10 | Foundations of Machine learning - Lecture Notes.pdf | 1 012 |
| 11 |  Variance and Covariance
in Machine Learning | 1 031 |
| 12 | An Introduction to Statistics with Python
Thomas Haslwanter, 2016 | 1 219 |
| 13 | Machine Learning Handwritten Notes💡.pdf | 1 200 |
| 14 |  Roadmap To Master Machine Learning | 1 240 |
| 15 |  Top Machine Learning algorithms | 1 104 |
| 16 | What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy. | 1 105 |
| 17 | What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform. | 939 |
| 18 | What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability | 894 |
| 19 | +4 Python code for Artificial Intelligence: Foundations of Computational Agents | 1 059 |
| 20 | +3 Top 100 NLP Questions .pdf | 956 |
