Planning for Data Science or Data Engineering Interview.
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
โค4๐2
Want to practice for your next interview?
Now see how it goes. All the best for your preparation
Like this post if you need more content like this๐โค๏ธ
Then use this prompt and ask Chat GPT to act as an interviewer ๐๐ (Tap to copy)
I want you to act as an interviewer. I will be the
candidate and you will ask me the
interview questions for the position position. I
want you to only reply as the interviewer.
Do not write all the conservation at once. I
want you to only do the interview with me.
Ask me the questions and wait for my answers.
Do not write explanations. Ask me the
questions one by one like an interviewer does
and wait for my answers. My first
sentence is "Hi"
Now see how it goes. All the best for your preparation
Like this post if you need more content like this๐โค๏ธ
โค4
Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence
1. Programming Languages:
Python
R
Java
Julia
2. AI Frameworks:
TensorFlow
PyTorch
Keras
MXNet
Caffe
3. Machine Learning Libraries:
Scikit-learn: For classical machine learning models.
XGBoost: For boosting algorithms.
LightGBM: For gradient boosting models.
4. Deep Learning Tools:
TensorFlow
PyTorch
Keras
Theano
5. Natural Language Processing (NLP) Tools:
NLTK (Natural Language Toolkit)
SpaCy
Hugging Face Transformers
Gensim
6. Computer Vision Libraries:
OpenCV
DLIB
Detectron2
7. Reinforcement Learning Frameworks:
Stable-Baselines3
RLlib
OpenAI Gym
8. AI Development Platforms:
IBM Watson
Google AI Platform
Microsoft AI
9. Data Visualization Tools:
Matplotlib
Seaborn
Plotly
Tableau
10. Robotics Frameworks:
ROS (Robot Operating System)
MoveIt!
11. Big Data Tools for AI:
Apache Spark
Hadoop
12. Cloud Platforms for AI Deployment:
Google Cloud AI
AWS SageMaker
Microsoft Azure AI
13. Popular AI APIs and Services:
Google Cloud Vision API
Microsoft Azure Cognitive Services
IBM Watson AI APIs
14. Learning Resources and Communities:
Kaggle
GitHub AI Projects
Papers with Code
ENJOY LEARNING ๐๐
1. Programming Languages:
Python
R
Java
Julia
2. AI Frameworks:
TensorFlow
PyTorch
Keras
MXNet
Caffe
3. Machine Learning Libraries:
Scikit-learn: For classical machine learning models.
XGBoost: For boosting algorithms.
LightGBM: For gradient boosting models.
4. Deep Learning Tools:
TensorFlow
PyTorch
Keras
Theano
5. Natural Language Processing (NLP) Tools:
NLTK (Natural Language Toolkit)
SpaCy
Hugging Face Transformers
Gensim
6. Computer Vision Libraries:
OpenCV
DLIB
Detectron2
7. Reinforcement Learning Frameworks:
Stable-Baselines3
RLlib
OpenAI Gym
8. AI Development Platforms:
IBM Watson
Google AI Platform
Microsoft AI
9. Data Visualization Tools:
Matplotlib
Seaborn
Plotly
Tableau
10. Robotics Frameworks:
ROS (Robot Operating System)
MoveIt!
11. Big Data Tools for AI:
Apache Spark
Hadoop
12. Cloud Platforms for AI Deployment:
Google Cloud AI
AWS SageMaker
Microsoft Azure AI
13. Popular AI APIs and Services:
Google Cloud Vision API
Microsoft Azure Cognitive Services
IBM Watson AI APIs
14. Learning Resources and Communities:
Kaggle
GitHub AI Projects
Papers with Code
ENJOY LEARNING ๐๐
๐4โค1
Top 10 Computer Vision Project Ideas
1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
โค1
12 Essential Math Theories for AI
Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge:
Curse of Dimensionality:
Challenges with high-dimensional data.
Law of Large Numbers:
Reliability improves with larger datasets.
Central Limit Theorem:
Sample means approach a normal distribution.
Bayes' Theorem:
Updates probabilities with new data.
Overfitting & Underfitting:
Finding balance in model complexity.
Gradient Descent:
Optimizes model performance.
Information Theory:
Efficient data compression.
Markov Decision Processes:
Models for decision-making.
Game Theory:
Insights on agent interactions.
Statistical Learning Theory:
Basis for prediction models.
Hebbian Theory:
Neural networks learning principles.
Convolution:
Image processing in AI.
Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.
Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge:
Curse of Dimensionality:
Challenges with high-dimensional data.
Law of Large Numbers:
Reliability improves with larger datasets.
Central Limit Theorem:
Sample means approach a normal distribution.
Bayes' Theorem:
Updates probabilities with new data.
Overfitting & Underfitting:
Finding balance in model complexity.
Gradient Descent:
Optimizes model performance.
Information Theory:
Efficient data compression.
Markov Decision Processes:
Models for decision-making.
Game Theory:
Insights on agent interactions.
Statistical Learning Theory:
Basis for prediction models.
Hebbian Theory:
Neural networks learning principles.
Convolution:
Image processing in AI.
Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.
๐4
Top 5 data science projects for freshers
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
๐3
Essential Python Libraries to build your career in Data Science ๐๐
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
๐5โค2
Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
Join for more: t.iss.one/datasciencefun
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
Join for more: t.iss.one/datasciencefun
๐6
Interview QnAs For ML Engineer
1.What are the various steps involved in an data analytics project?
The steps involved in a data analytics project are:
Data collection
Data cleansing
Data pre-processing
EDA
Creation of train test and validation sets
Model creation
Hyperparameter tuning
Model deployment
2. Explain Star Schema.
Star schema is a data warehousing concept in which all schema is connected to a central schema.
3. What is root cause analysis?
Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. Itโs generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes.
4. Define Confounding Variables.
A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable :
Variables should be correlated to the independent variable.
Variables should be informally related to the dependent variable.
For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.
1.What are the various steps involved in an data analytics project?
The steps involved in a data analytics project are:
Data collection
Data cleansing
Data pre-processing
EDA
Creation of train test and validation sets
Model creation
Hyperparameter tuning
Model deployment
2. Explain Star Schema.
Star schema is a data warehousing concept in which all schema is connected to a central schema.
3. What is root cause analysis?
Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. Itโs generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes.
4. Define Confounding Variables.
A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable :
Variables should be correlated to the independent variable.
Variables should be informally related to the dependent variable.
For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.
๐6โค3
10 Python Libraries Every AI Developer Should Know
โ NumPy โ Foundation for numerical computing in Python
โ Pandas โ Data manipulation and analysis made easy
โ Scikit-learn โ Powerful library for classical ML models
โ TensorFlow โ End-to-end open-source ML platform by Google
โ PyTorch โ Deep learning framework loved by researchers
โ Matplotlib โ Create stunning data visualizations
โ Seaborn โ High-level interface for drawing statistical plots
โ NLTK โ Toolkit for working with human language data (NLP)
โ OpenCV โ Real-time computer vision made simple
โ Hugging Face Transformers โ Pretrained models for NLP, CV, and more
React with โค๏ธ for more
โ NumPy โ Foundation for numerical computing in Python
โ Pandas โ Data manipulation and analysis made easy
โ Scikit-learn โ Powerful library for classical ML models
โ TensorFlow โ End-to-end open-source ML platform by Google
โ PyTorch โ Deep learning framework loved by researchers
โ Matplotlib โ Create stunning data visualizations
โ Seaborn โ High-level interface for drawing statistical plots
โ NLTK โ Toolkit for working with human language data (NLP)
โ OpenCV โ Real-time computer vision made simple
โ Hugging Face Transformers โ Pretrained models for NLP, CV, and more
React with โค๏ธ for more
๐4โค3
10 New & Trending AI Concepts You Should Know in 2025
โ Retrieval-Augmented Generation (RAG) โ Combines search with generative AI for smarter answers
โ Multi-Modal Models โ AI that understands text, image, audio, and video (like GPT-4V, Gemini)
โ Agents & AutoGPT โ AI that can plan, execute, and make decisions with minimal input
โ Synthetic Data Generation โ Creating fake yet realistic data to train AI models
โ Federated Learning โ Train models without moving your data (privacy-first AI)
โ Prompt Engineering โ Crafting prompts to get the best out of LLMs
โ Fine-Tuning & LoRA โ Customize big models for specific tasks with minimal resources
โ AI Safety & Alignment โ Making sure AI systems behave ethically and predictably
โ TinyML โ Running ML models on edge devices with very low power (IoT focus)
โ Open-Source LLMs โ Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
โ Retrieval-Augmented Generation (RAG) โ Combines search with generative AI for smarter answers
โ Multi-Modal Models โ AI that understands text, image, audio, and video (like GPT-4V, Gemini)
โ Agents & AutoGPT โ AI that can plan, execute, and make decisions with minimal input
โ Synthetic Data Generation โ Creating fake yet realistic data to train AI models
โ Federated Learning โ Train models without moving your data (privacy-first AI)
โ Prompt Engineering โ Crafting prompts to get the best out of LLMs
โ Fine-Tuning & LoRA โ Customize big models for specific tasks with minimal resources
โ AI Safety & Alignment โ Making sure AI systems behave ethically and predictably
โ TinyML โ Running ML models on edge devices with very low power (IoT focus)
โ Open-Source LLMs โ Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
๐5
5 Trending AI Jobs You Canโt Miss in 2025! ๐ค
๐ป *Machine Learning Engineer*
๐๐ป *Average Salary:* $114,000
๐๐ป *What They Do:* Design and implement ML algorithms while collaborating with data scientists and engineers. ๐
๐ *Data Scientist*
๐๐ป *Average Salary:* $120,000
๐๐ป *What They Do:* Analyze data, build predictive models, and drive data-backed decisions. ๐
๐ฌ *AI Research Scientist*
๐๐ป *Average Salary:* $126,000
๐๐ป *What They Do:* Explore the future of AI by testing algorithms and driving innovation. ๐
๐ค *AI Ethic*
๐๐ป *Average Salary:* $135,000
๐๐ป *What They Do:* Promote ethical AI development, address biases, and ensure fairness. ๐
๐ *AI Product Manager*
๐๐ป *Average Salary:* $140,000
๐๐ป *What They Do:* Manage AI products for success, focusing on innovation and ethical impact. ๐
๐ป *Machine Learning Engineer*
๐๐ป *Average Salary:* $114,000
๐๐ป *What They Do:* Design and implement ML algorithms while collaborating with data scientists and engineers. ๐
๐ *Data Scientist*
๐๐ป *Average Salary:* $120,000
๐๐ป *What They Do:* Analyze data, build predictive models, and drive data-backed decisions. ๐
๐ฌ *AI Research Scientist*
๐๐ป *Average Salary:* $126,000
๐๐ป *What They Do:* Explore the future of AI by testing algorithms and driving innovation. ๐
๐ค *AI Ethic*
๐๐ป *Average Salary:* $135,000
๐๐ป *What They Do:* Promote ethical AI development, address biases, and ensure fairness. ๐
๐ *AI Product Manager*
๐๐ป *Average Salary:* $140,000
๐๐ป *What They Do:* Manage AI products for success, focusing on innovation and ethical impact. ๐
๐6
Time Complexity of 10 Most Popular ML Algorithms
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1๏ธโฃ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2๏ธโฃ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3๏ธโฃ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4๏ธโฃ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5๏ธโฃ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.
For instance,
1๏ธโฃ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.
2๏ธโฃ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.
3๏ธโฃ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.
4๏ธโฃ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.
5๏ธโฃ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.
๐5
ยฉHow fresher can get a job as a data scientist?ยฉ
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
โค5
Artificial Intelligence on WhatsApp ๐
Top AI Channels on WhatsApp!
1. ChatGPT โ Your go-to AI for anything and everything. https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
2. OpenAI โ Your gateway to cutting-edge artificial intelligence innovation. https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
3. Microsoft Copilot โ Your productivity powerhouse. https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
4. Perplexity AI โ Your AI-powered research buddy with real-time answers. https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
5. Generative AI โ Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
6. Prompt Engineering โ Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
7. AI Tools โ Your toolkit for automating, analyzing, and accelerating everything. https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
8. AI Studio โ Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini โ Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning โ Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. Data Science Projects โ Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208
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Top AI Channels on WhatsApp!
1. ChatGPT โ Your go-to AI for anything and everything. https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
2. OpenAI โ Your gateway to cutting-edge artificial intelligence innovation. https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
3. Microsoft Copilot โ Your productivity powerhouse. https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
4. Perplexity AI โ Your AI-powered research buddy with real-time answers. https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
5. Generative AI โ Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
6. Prompt Engineering โ Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
7. AI Tools โ Your toolkit for automating, analyzing, and accelerating everything. https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
8. AI Studio โ Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini โ Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning โ Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. Data Science Projects โ Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208
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Guys, Big Announcement! ๐
We've officially hit 3 Lakh subscribers on WhatsAppโ and it's time to kick off the next big learning journey together! ๐คฉ
Artificial Intelligence Complete Series โ a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!
This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.
Hereโs what weโll cover:
*Week 1: Introduction to AI*
- What is AI? Understanding the basics without the jargon
- Types of AI: Narrow vs. General AI
- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)
- Real-world applications: From Chatbots to Self-Driving Cars ๐
- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)
*Week 2: Core AI Techniques*
- Supervised vs. Unsupervised Learning
- Understanding Data: The backbone of AI
- Linear Regression: Your first AI algorithm!
- Decision Trees, K-Nearest Neighbors, and Support Vector Machines
- Hands-on project: Building a basic classifier with Python ๐
*Week 3: Deep Dive into Machine Learning*
- What makes ML different from AI?
- Gradient Descent & Model Optimization
- Evaluating Models: Accuracy, Precision, Recall, and F1-Score
- Hyperparameter Tuning
- Hands-on project: Building a predictive model with real data ๐
*Week 4: Introduction to Neural Networks*
- The fundamentals of neural networks & deep learning
- Understanding how a neural network mimics the human brain ๐ง
- Training your first Neural Network with TensorFlow
- Introduction to Backpropagation and Activation Functions
- Hands-on project: Build a simple neural network to recognize images ๐ธ
*Week 5: Advanced AI Concepts*
- Natural Language Processing (NLP): Teach machines to understand text and speech ๐ฃ๏ธ
- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)
- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)
- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more
- Hands-on project: Implementing NLP for text classification ๐
*Week 6: Building Real-World AI Applications*
- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection
- Integrating AI with APIs and Web Services
- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects
- Hands-on project: Build a recommendation system like Netflix ๐ฌ
*Week 7: Preparing for AI Careers*
- Common interview questions for AI & ML roles ๐
- Building an AI Portfolio: Showcase your projects
- Understanding AI in Industry: How itโs transforming businesses
- Networking and building your career in AI ๐
Join our WhatsApp channel to access it for FREE: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y/1031
We've officially hit 3 Lakh subscribers on WhatsAppโ and it's time to kick off the next big learning journey together! ๐คฉ
Artificial Intelligence Complete Series โ a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!
This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.
Hereโs what weโll cover:
*Week 1: Introduction to AI*
- What is AI? Understanding the basics without the jargon
- Types of AI: Narrow vs. General AI
- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)
- Real-world applications: From Chatbots to Self-Driving Cars ๐
- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)
*Week 2: Core AI Techniques*
- Supervised vs. Unsupervised Learning
- Understanding Data: The backbone of AI
- Linear Regression: Your first AI algorithm!
- Decision Trees, K-Nearest Neighbors, and Support Vector Machines
- Hands-on project: Building a basic classifier with Python ๐
*Week 3: Deep Dive into Machine Learning*
- What makes ML different from AI?
- Gradient Descent & Model Optimization
- Evaluating Models: Accuracy, Precision, Recall, and F1-Score
- Hyperparameter Tuning
- Hands-on project: Building a predictive model with real data ๐
*Week 4: Introduction to Neural Networks*
- The fundamentals of neural networks & deep learning
- Understanding how a neural network mimics the human brain ๐ง
- Training your first Neural Network with TensorFlow
- Introduction to Backpropagation and Activation Functions
- Hands-on project: Build a simple neural network to recognize images ๐ธ
*Week 5: Advanced AI Concepts*
- Natural Language Processing (NLP): Teach machines to understand text and speech ๐ฃ๏ธ
- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)
- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)
- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more
- Hands-on project: Implementing NLP for text classification ๐
*Week 6: Building Real-World AI Applications*
- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection
- Integrating AI with APIs and Web Services
- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects
- Hands-on project: Build a recommendation system like Netflix ๐ฌ
*Week 7: Preparing for AI Careers*
- Common interview questions for AI & ML roles ๐
- Building an AI Portfolio: Showcase your projects
- Understanding AI in Industry: How itโs transforming businesses
- Networking and building your career in AI ๐
Join our WhatsApp channel to access it for FREE: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y/1031
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