A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Data Science Interview Resources
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https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Data Science Interview Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
๐2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐ช๐ถ๐น๐น ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
๐ Want to Learn Data Analytics but Hate the High Price Tags?๐ฐ๐
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
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All The Best ๐
๐ Want to Learn Data Analytics but Hate the High Price Tags?๐ฐ๐
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
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All The Best ๐
Intro to Python for Computer Science.pdf
17.5 MB
Intro to Python for Computer Science and Data Science
Paul & Harvey Deitel, 2022
Paul & Harvey Deitel, 2022
Python_in_a_Nutshell_A_Desktop_Quick_Reference (1).pdf
5.8 MB
Python in a Nutshell
Alex Martelli, 2023
Alex Martelli, 2023
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Forwarded from Artificial Intelligence
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Harvard University is one of the worldโs most prestigious universities, and it offers a wide range of free online courses through its HarvardX program. Courses range from computer science and business to humanities and social sciences.
https://pll.harvard.edu/catalog/free
*2. Massachusetts Institute of Technology (MIT)*
MIT is a renowned institution in the field of technology, and it offers free online courses in engineering, computer science, data science, and more through its OpenCourseWare platform.
https://ocw.mit.edu/search/
*3. Stanford University*
Stanford University is a world-renowned research institution, and it offers a variety of free online courses through its OpenEdX platform. Courses include computer science, engineering, and social sciences.
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Forwarded from Artificial Intelligence
๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ & ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐ง๐ผ๐ฝ ๐๐ผ๐ฏ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
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Here are 8 concise tips to help you ace a technical AI engineering interview:
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
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๐1
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ฆ๐ธ๐๐ฟ๐ผ๐ฐ๐ธ๐ฒ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Whether youโre a beginner, career switcher, or just curious about data analytics, these 5 free online courses are your perfect starting point!๐ฏ
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Whether youโre a beginner, career switcher, or just curious about data analytics, these 5 free online courses are your perfect starting point!๐ฏ
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Breaking into Data Science doesnโt need to be complicated.
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
๐4
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Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
โค2
Forwarded from Python Projects & Resources
๐ฒ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐๐๐๐ฟ๐ฒ-๐ฃ๐ฟ๐ผ๐ผ๐ณ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to Stay Ahead in 2025? Learn These 6 In-Demand Skills for FREE!๐
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Enjoy Learning โ ๏ธ
Want to Stay Ahead in 2025? Learn These 6 In-Demand Skills for FREE!๐
The future of work is evolving fast, and mastering the right skills today can set you up for big success tomorrow๐ฏ
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
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
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs 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://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
### 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
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs 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://t.iss.one/free4unow_backup
ENJOY LEARNING ๐๐
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Forwarded from Python Projects & Resources
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ฎ๐ป ๐ง๐ฎ๐ธ๐ฒ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
MIT is known for world-class educationโbut you donโt need to walk its halls to access its knowledge๐จโ๐ป๐
Thanks to edX, anyone can enroll in these free MIT-certified courses from anywhere in the world๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/43eM8I2
Letโs explore 5 of the best free courses MIT has to offerโ ๏ธ
MIT is known for world-class educationโbut you donโt need to walk its halls to access its knowledge๐จโ๐ป๐
Thanks to edX, anyone can enroll in these free MIT-certified courses from anywhere in the world๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/43eM8I2
Letโs explore 5 of the best free courses MIT has to offerโ ๏ธ
Use Chat GPT to prepare for your next Interview
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
๐กPrompt 1: Here is a Job description of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
๐กPrompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
๐กPrompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
๐กPrompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
๐กPrompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://t.iss.one/InterviewBooks/166
ENJOY LEARNING ๐๐
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
๐กPrompt 1: Here is a Job description of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
๐กPrompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
๐กPrompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
๐กPrompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
๐กPrompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://t.iss.one/InterviewBooks/166
ENJOY LEARNING ๐๐
๐4โค1
๐๐ฟ๐ฒ๐ฒ ๐ข๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
Hereโs your chance to build a solid foundation in artificial intelligence with the Oracle AI Foundations Associate course โ absolutely FREE!๐ป๐
๐๐ข๐ง๐ค๐:-
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No registration fee. No prior AI experience needed. Just pure learning to future-proof your career!โ ๏ธ
Hereโs your chance to build a solid foundation in artificial intelligence with the Oracle AI Foundations Associate course โ absolutely FREE!๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FfFOrC
No registration fee. No prior AI experience needed. Just pure learning to future-proof your career!โ ๏ธ
๐1
Forwarded from Artificial Intelligence
๐ณ+ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
Hereโs your golden chance to upskill with free, industry-recognized certifications from Googleโall without spending a rupee!๐ฐ๐
These beginner-friendly courses cover everything from digital marketing to data tools like Google Ads, Analytics, and moreโฌ๏ธ
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Hereโs your golden chance to upskill with free, industry-recognized certifications from Googleโall without spending a rupee!๐ฐ๐
These beginner-friendly courses cover everything from digital marketing to data tools like Google Ads, Analytics, and moreโฌ๏ธ
๐๐ข๐ง๐ค๐:-
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Tag them or share this post!โ ๏ธ
๐1