Top 10 machine Learning algorithms
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
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1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
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1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesnโt affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example โ students having grades of more than 70%.
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesnโt affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
5. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example โ students having grades of more than 70%.
โค2
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Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
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1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
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The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
Step 4: Get into Transformers architecture
Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Faceโs Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.
Step 5: Working with Pre-trained Large Language Models
Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.
Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.
Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
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Here's how you can start with AI engineering with zero experience!
The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
Step 4: Get into Transformers architecture
Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Faceโs Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.
Step 5: Working with Pre-trained Large Language Models
Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.
Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.
Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others.
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๐ง Technologies for Data Analysts!
๐ Data Manipulation & Analysis
โช๏ธ Excel โ Spreadsheet Data Analysis & Visualization
โช๏ธ SQL โ Structured Query Language for Data Extraction
โช๏ธ Pandas (Python) โ Data Analysis with DataFrames
โช๏ธ NumPy (Python) โ Numerical Computing for Large Datasets
โช๏ธ Google Sheets โ Online Collaboration for Data Analysis
๐ Data Visualization
โช๏ธ Power BI โ Business Intelligence & Dashboarding
โช๏ธ Tableau โ Interactive Data Visualization
โช๏ธ Matplotlib (Python) โ Plotting Graphs & Charts
โช๏ธ Seaborn (Python) โ Statistical Data Visualization
โช๏ธ Google Data Studio โ Free, Web-Based Visualization Tool
๐ ETL (Extract, Transform, Load)
โช๏ธ SQL Server Integration Services (SSIS) โ Data Integration & ETL
โช๏ธ Apache NiFi โ Automating Data Flows
โช๏ธ Talend โ Data Integration for Cloud & On-premises
๐งน Data Cleaning & Preparation
โช๏ธ OpenRefine โ Clean & Transform Messy Data
โช๏ธ Pandas Profiling (Python) โ Data Profiling & Preprocessing
โช๏ธ DataWrangler โ Data Transformation Tool
๐ฆ Data Storage & Databases
โช๏ธ SQL โ Relational Databases (MySQL, PostgreSQL, MS SQL)
โช๏ธ NoSQL (MongoDB) โ Flexible, Schema-less Data Storage
โช๏ธ Google BigQuery โ Scalable Cloud Data Warehousing
โช๏ธ Redshift โ Amazonโs Cloud Data Warehouse
โ๏ธ Data Automation
โช๏ธ Alteryx โ Data Blending & Advanced Analytics
โช๏ธ Knime โ Data Analytics & Reporting Automation
โช๏ธ Zapier โ Connect & Automate Data Workflows
๐ Advanced Analytics & Statistical Tools
โช๏ธ R โ Statistical Computing & Analysis
โช๏ธ Python (SciPy, Statsmodels) โ Statistical Modeling & Hypothesis Testing
โช๏ธ SPSS โ Statistical Software for Data Analysis
โช๏ธ SAS โ Advanced Analytics & Predictive Modeling
๐ Collaboration & Reporting
โช๏ธ Power BI Service โ Online Sharing & Collaboration for Dashboards
โช๏ธ Tableau Online โ Cloud-Based Visualization & Sharing
โช๏ธ Google Analytics โ Web Traffic Data Insights
โช๏ธ Trello / JIRA โ Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React โค๏ธ for more
๐ Data Manipulation & Analysis
โช๏ธ Excel โ Spreadsheet Data Analysis & Visualization
โช๏ธ SQL โ Structured Query Language for Data Extraction
โช๏ธ Pandas (Python) โ Data Analysis with DataFrames
โช๏ธ NumPy (Python) โ Numerical Computing for Large Datasets
โช๏ธ Google Sheets โ Online Collaboration for Data Analysis
๐ Data Visualization
โช๏ธ Power BI โ Business Intelligence & Dashboarding
โช๏ธ Tableau โ Interactive Data Visualization
โช๏ธ Matplotlib (Python) โ Plotting Graphs & Charts
โช๏ธ Seaborn (Python) โ Statistical Data Visualization
โช๏ธ Google Data Studio โ Free, Web-Based Visualization Tool
๐ ETL (Extract, Transform, Load)
โช๏ธ SQL Server Integration Services (SSIS) โ Data Integration & ETL
โช๏ธ Apache NiFi โ Automating Data Flows
โช๏ธ Talend โ Data Integration for Cloud & On-premises
๐งน Data Cleaning & Preparation
โช๏ธ OpenRefine โ Clean & Transform Messy Data
โช๏ธ Pandas Profiling (Python) โ Data Profiling & Preprocessing
โช๏ธ DataWrangler โ Data Transformation Tool
๐ฆ Data Storage & Databases
โช๏ธ SQL โ Relational Databases (MySQL, PostgreSQL, MS SQL)
โช๏ธ NoSQL (MongoDB) โ Flexible, Schema-less Data Storage
โช๏ธ Google BigQuery โ Scalable Cloud Data Warehousing
โช๏ธ Redshift โ Amazonโs Cloud Data Warehouse
โ๏ธ Data Automation
โช๏ธ Alteryx โ Data Blending & Advanced Analytics
โช๏ธ Knime โ Data Analytics & Reporting Automation
โช๏ธ Zapier โ Connect & Automate Data Workflows
๐ Advanced Analytics & Statistical Tools
โช๏ธ R โ Statistical Computing & Analysis
โช๏ธ Python (SciPy, Statsmodels) โ Statistical Modeling & Hypothesis Testing
โช๏ธ SPSS โ Statistical Software for Data Analysis
โช๏ธ SAS โ Advanced Analytics & Predictive Modeling
๐ Collaboration & Reporting
โช๏ธ Power BI Service โ Online Sharing & Collaboration for Dashboards
โช๏ธ Tableau Online โ Cloud-Based Visualization & Sharing
โช๏ธ Google Analytics โ Web Traffic Data Insights
โช๏ธ Trello / JIRA โ Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React โค๏ธ for more
โค2
๐๐๐น๐น๐๐๐ฎ๐ฐ๐ธ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ ๐๐ฅ๐๐ ๐๐ฒ๐บ๐ผ ๐๐น๐ฎ๐๐ ๐๐ป ๐ฃ๐๐ป๐ฒ๐
Master Coding Skills & Get Your Dream Job In Top Tech Companies
Designed by the Top 1% from IITs and top MNCs.
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- Unlock Opportunities With 500+ Hiring Partners
- 100% Placement assistance
- 60+ hiring drives each month
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
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Location:- Baner, Pune
Master Coding Skills & Get Your Dream Job In Top Tech Companies
Designed by the Top 1% from IITs and top MNCs.
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- Unlock Opportunities With 500+ Hiring Partners
- 100% Placement assistance
- 60+ hiring drives each month
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/3YA32zi
Location:- Baner, Pune
15 Coding Project Ideas ๐
Beginner Level:
1. ๐๏ธ File Organizer Script
2. ๐งพ Expense Tracker (CLI or GUI)
3. ๐ Password Generator
4. ๐ Simple Calendar App
5. ๐น๏ธ Number Guessing Game
Intermediate Level:
6. ๐ฐ News Aggregator using API
7. ๐ง Email Sender App
8. ๐ณ๏ธ Polling/Voting System
9. ๐งโ๐ Student Management System
10. ๐ท๏ธ URL Shortener
Advanced Level:
11. ๐ฃ๏ธ Real-Time Chat App (with backend)
12. ๐ฆ Inventory Management System
13. ๐ฆ Budgeting App with Charts
14. ๐ฅ Appointment Booking System
15. ๐ง AI-powered Text Summarizer
Credits: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
React โค๏ธ for more
Beginner Level:
1. ๐๏ธ File Organizer Script
2. ๐งพ Expense Tracker (CLI or GUI)
3. ๐ Password Generator
4. ๐ Simple Calendar App
5. ๐น๏ธ Number Guessing Game
Intermediate Level:
6. ๐ฐ News Aggregator using API
7. ๐ง Email Sender App
8. ๐ณ๏ธ Polling/Voting System
9. ๐งโ๐ Student Management System
10. ๐ท๏ธ URL Shortener
Advanced Level:
11. ๐ฃ๏ธ Real-Time Chat App (with backend)
12. ๐ฆ Inventory Management System
13. ๐ฆ Budgeting App with Charts
14. ๐ฅ Appointment Booking System
15. ๐ง AI-powered Text Summarizer
Credits: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
React โค๏ธ for more
โค2
๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ข๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified ๐
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/40OgK1w
Enroll For FREE & Get Certified ๐
Java Developer Interview โค
It'll gonna be super helpful for YOU
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ญ: ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ณ๐น๐ผ๐ ๐ฎ๐ป๐ฑ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
- Please tell me about your project and its architecture, Challenges faced?
- What was your role in the project? Tech Stack of project? why this stack?
- Problem you solved during the project? How collaboration within the team?
- What lessons did you learn from working on this project?
- If you could go back, what would you do differently in this project?
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฎ: ๐๐ผ๐ฟ๐ฒ ๐๐ฎ๐๐ฎ
- String Concepts/Hashcode- Equal Methods
- Immutability
- OOPS concepts
- Serialization
- Collection Framework
- Exception Handling
- Multithreading
- Java Memory Model
- Garbage collection
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฏ: ๐๐ฎ๐๐ฎ-๐ด/๐๐ฎ๐๐ฎ-๐ญ๐ญ/๐๐ฎ๐๐ฎ๐ญ๐ณ
- Java 8 features
- Default/Static methods
- Lambda expression
- Functional interfaces
- Optional API
- Stream API
- Pattern matching
- Text block
- Modules
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฐ: ๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ, ๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด-๐๐ผ๐ผ๐, ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ, ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐๐ ๐๐ฃ๐
- Dependency Injection/IOC, Spring MVC
- Configuration, Annotations, CRUD
- Bean, Scopes, Profiles, Bean lifecycle
- App context/Bean context
- AOP, Exception Handler, Control Advice
- Security (JWT, Oauth)
- Actuators
- WebFlux and Mono Framework
- HTTP methods
- JPA
- Microservice concepts
- Spring Cloud
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฑ: ๐๐ถ๐ฏ๐ฒ๐ฟ๐ป๐ฎ๐๐ฒ/๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด-๐ฑ๐ฎ๐๐ฎ ๐๐ฝ๐ฎ/๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ (๐ฆ๐ค๐ ๐ผ๐ฟ ๐ก๐ผ๐ฆ๐ค๐)
- JPA Repositories
- Relationship with Entities
- SQL queries on Employee department
- Queries, Highest Nth salary queries
- Relational and No-Relational DB concepts
- CRUD operations in DB
- Joins, indexing, procs, function
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฒ: ๐๐ผ๐ฑ๐ถ๐ป๐ด
- DSA Related Questions
- Sorting and searching using Java API.
- Stream API coding Questions
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ณ: ๐๐ฒ๐๐ผ๐ฝ๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ผ๐ป ๐ฑ๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐ ๐ง๐ผ๐ผ๐น๐
- These types of topics are mostly asked by managers or leads who are heavily working on it, That's why they may grill you on DevOps/deployment-related tools, You should have an understanding of common tools like Jenkins, Kubernetes, Kafka, Cloud, and all.
๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ด: ๐๐ฒ๐๐ ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ
- The interviewer always wanted to ask about some design patterns, it may be Normal design patterns like singleton, factory, or observer patterns to know that you can use these in coding.
Make sure to scroll through the above messages ๐ definitely you will get the more interesting things ๐ค
All the best ๐๐
It'll gonna be super helpful for YOU
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ญ: ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ณ๐น๐ผ๐ ๐ฎ๐ป๐ฑ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ
- Please tell me about your project and its architecture, Challenges faced?
- What was your role in the project? Tech Stack of project? why this stack?
- Problem you solved during the project? How collaboration within the team?
- What lessons did you learn from working on this project?
- If you could go back, what would you do differently in this project?
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฎ: ๐๐ผ๐ฟ๐ฒ ๐๐ฎ๐๐ฎ
- String Concepts/Hashcode- Equal Methods
- Immutability
- OOPS concepts
- Serialization
- Collection Framework
- Exception Handling
- Multithreading
- Java Memory Model
- Garbage collection
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฏ: ๐๐ฎ๐๐ฎ-๐ด/๐๐ฎ๐๐ฎ-๐ญ๐ญ/๐๐ฎ๐๐ฎ๐ญ๐ณ
- Java 8 features
- Default/Static methods
- Lambda expression
- Functional interfaces
- Optional API
- Stream API
- Pattern matching
- Text block
- Modules
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฐ: ๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ, ๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด-๐๐ผ๐ผ๐, ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ, ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐๐ ๐๐ฃ๐
- Dependency Injection/IOC, Spring MVC
- Configuration, Annotations, CRUD
- Bean, Scopes, Profiles, Bean lifecycle
- App context/Bean context
- AOP, Exception Handler, Control Advice
- Security (JWT, Oauth)
- Actuators
- WebFlux and Mono Framework
- HTTP methods
- JPA
- Microservice concepts
- Spring Cloud
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฑ: ๐๐ถ๐ฏ๐ฒ๐ฟ๐ป๐ฎ๐๐ฒ/๐ฆ๐ฝ๐ฟ๐ถ๐ป๐ด-๐ฑ๐ฎ๐๐ฎ ๐๐ฝ๐ฎ/๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ (๐ฆ๐ค๐ ๐ผ๐ฟ ๐ก๐ผ๐ฆ๐ค๐)
- JPA Repositories
- Relationship with Entities
- SQL queries on Employee department
- Queries, Highest Nth salary queries
- Relational and No-Relational DB concepts
- CRUD operations in DB
- Joins, indexing, procs, function
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ฒ: ๐๐ผ๐ฑ๐ถ๐ป๐ด
- DSA Related Questions
- Sorting and searching using Java API.
- Stream API coding Questions
๐ง๐ผ๐ฝ๐ถ๐ฐ ๐ณ: ๐๐ฒ๐๐ผ๐ฝ๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ผ๐ป ๐ฑ๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐ ๐ง๐ผ๐ผ๐น๐
- These types of topics are mostly asked by managers or leads who are heavily working on it, That's why they may grill you on DevOps/deployment-related tools, You should have an understanding of common tools like Jenkins, Kubernetes, Kafka, Cloud, and all.
๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ด: ๐๐ฒ๐๐ ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ
- The interviewer always wanted to ask about some design patterns, it may be Normal design patterns like singleton, factory, or observer patterns to know that you can use these in coding.
Make sure to scroll through the above messages ๐ definitely you will get the more interesting things ๐ค
All the best ๐๐
โค2
๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ป ๐ง๐ผ๐ฝ ๐ ๐ก๐๐๐
Learn Data Analytics, Data Science & AI From Top Data Experts
Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- 12.65 Lakhs Highest Salary
- 500+ Partner Companies
- 100% Job Assistance
- 5.7 LPA Average Salary
๐๐ผ๐ผ๐ธ ๐ฎ ๐๐ฅ๐๐ ๐๐ผ๐๐ป๐๐ฒ๐น๐น๐ถ๐ป๐ด ๐ฆ๐ฒ๐๐๐ถ๐ผ๐ป๐ :
https://bit.ly/4g3kyT6
(Hurry Up๐โโ๏ธ. Limited Slots )
Learn Data Analytics, Data Science & AI From Top Data Experts
Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- 12.65 Lakhs Highest Salary
- 500+ Partner Companies
- 100% Job Assistance
- 5.7 LPA Average Salary
๐๐ผ๐ผ๐ธ ๐ฎ ๐๐ฅ๐๐ ๐๐ผ๐๐ป๐๐ฒ๐น๐น๐ถ๐ป๐ด ๐ฆ๐ฒ๐๐๐ถ๐ผ๐ป๐ :
https://bit.ly/4g3kyT6
(Hurry Up๐โโ๏ธ. Limited Slots )
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.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React โค๏ธ for more free resources
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.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React โค๏ธ for more free resources
โค1
Forwarded from Data Analytics
๐ ๐ง๐ผ๐ฝ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ๐ โ ๐๐ฅ๐๐ & ๐ข๐ป๐น๐ถ๐ป๐ฒ๐
Boost your resume with real-world experience from global giants! ๐ผ๐
๐น Deloitte โ https://pdlink.in/4iKcgA4
๐น Accenture โ https://pdlink.in/44pfljI
๐น TATA โ https://pdlink.in/3FyjDgp
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โจ 100% Virtual
๐ Certificate Included
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๐ Great for Beginners & Students
Apply now and gain an edge in your career! ๐๐
Boost your resume with real-world experience from global giants! ๐ผ๐
๐น Deloitte โ https://pdlink.in/4iKcgA4
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๐น TATA โ https://pdlink.in/3FyjDgp
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โค1
Frontend web development:
https://www.w3schools.com/html
https://www.w3schools.com/css
https://www.jschallenger.com
https://javascript30.com
https://t.iss.one/webdevcoursefree/110
https://t.iss.one/Programming_experts/107
Backend development:
https://learnpython.org/
https://t.iss.one/pythondevelopersindia/314
https://www.geeksforgeeks.org/java/
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://docs.microsoft.com/en-us/shows/beginners-series-to-nodejs/?languages=nodejs
Database:
https://mode.com/sql-tutorial/introduction-to-sql
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
https://books.goalkicker.com/MySQLBook/MySQLNotesForProfessionals.pdf
https://docs.oracle.com/cd/B19306_01/server.102/b14200.pdf
https://leetcode.com/problemset/database/
Cloud Computing:
https://bit.ly/3aoxt1N
https://t.iss.one/free4unow_backup/366
UI/UX:
https://www.freecodecamp.org/learn/responsive-web-design/
https://bit.ly/3r6F9xE
ENJOY LEARNING ๐๐
https://www.w3schools.com/html
https://www.w3schools.com/css
https://www.jschallenger.com
https://javascript30.com
https://t.iss.one/webdevcoursefree/110
https://t.iss.one/Programming_experts/107
Backend development:
https://learnpython.org/
https://t.iss.one/pythondevelopersindia/314
https://www.geeksforgeeks.org/java/
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://docs.microsoft.com/en-us/shows/beginners-series-to-nodejs/?languages=nodejs
Database:
https://mode.com/sql-tutorial/introduction-to-sql
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
https://books.goalkicker.com/MySQLBook/MySQLNotesForProfessionals.pdf
https://docs.oracle.com/cd/B19306_01/server.102/b14200.pdf
https://leetcode.com/problemset/database/
Cloud Computing:
https://bit.ly/3aoxt1N
https://t.iss.one/free4unow_backup/366
UI/UX:
https://www.freecodecamp.org/learn/responsive-web-design/
https://bit.ly/3r6F9xE
ENJOY LEARNING ๐๐
โค1
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
SQL:- https://pdlink.in/4lvR4zF
AWS:- https://pdlink.in/4nriVCH
Cybersecurity:- https://pdlink.in/3T6pg8O
Data Analytics:- https://pdlink.in/43TGwnM
Enroll for FREE & Get Certified ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
SQL:- https://pdlink.in/4lvR4zF
AWS:- https://pdlink.in/4nriVCH
Cybersecurity:- https://pdlink.in/3T6pg8O
Data Analytics:- https://pdlink.in/43TGwnM
Enroll for FREE & Get Certified ๐