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.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
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Hope this helps you ๐
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.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐15โค4
SQL Projects for Beginners to boost their resume!
1. Employee payroll management system
https://github.com/ojasphansekar/Employee-Payroll-Management-System
2. Library Management System
https://github.com/AlexanderWong/Library-Management-System
3. Student Database Management
https://github.com/shardul08/Student-DataBase-Management-System
4. SQL For Data Analysis Full Portfolio Project
https://youtube.com/watch?v=zZpMvAedh_E&ab_channel=WsCubeTech
5. Railway System Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/railway_system.html
More-> @dataportfolio
6. Inventory Control Management
https://vikingpathak.github.io/kh-sql-projects/markdown_files/inventory_control_management.html
7. Online Retail Application Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/online_retail_app.html
8. Employee-Payroll-Management-System
https://github.com/ojasphansekar/Employee-Payroll-Management-System
Free SQL Resources๐ https://t.iss.one/sqlanalyst
1. Employee payroll management system
https://github.com/ojasphansekar/Employee-Payroll-Management-System
2. Library Management System
https://github.com/AlexanderWong/Library-Management-System
3. Student Database Management
https://github.com/shardul08/Student-DataBase-Management-System
4. SQL For Data Analysis Full Portfolio Project
https://youtube.com/watch?v=zZpMvAedh_E&ab_channel=WsCubeTech
5. Railway System Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/railway_system.html
More-> @dataportfolio
6. Inventory Control Management
https://vikingpathak.github.io/kh-sql-projects/markdown_files/inventory_control_management.html
7. Online Retail Application Database
https://vikingpathak.github.io/kh-sql-projects/markdown_files/online_retail_app.html
8. Employee-Payroll-Management-System
https://github.com/ojasphansekar/Employee-Payroll-Management-System
Free SQL Resources๐ https://t.iss.one/sqlanalyst
๐9โค4
Data Science With Python Workflow Cheat Sheet
Creator: business Science
Stars โญ๏ธ: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf
Creator: business Science
Stars โญ๏ธ: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf
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Join our WhatsApp channel before we reach 10k
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https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react ๐โค๏ธ if you guys want me to share more of this content...
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react ๐โค๏ธ if you guys want me to share more of this content...
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๐ Data Science Project Ideas for Freshers
Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Free datasets to build the projects
๐๐
https://t.iss.one/datasciencefun/1126
ENJOY LEARNING ๐๐
Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Free datasets to build the projects
๐๐
https://t.iss.one/datasciencefun/1126
ENJOY LEARNING ๐๐
โค1
Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.
Let's talk about it ๐งต
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
โช๏ธ Normalization
โช๏ธ Standardization
The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.
Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.
This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.
In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).
More about them:
โช๏ธStandardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
โช๏ธStandardization is robust to outliers.
โช๏ธNormalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.
Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.
https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing
Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.
https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk
Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.
Enable gradient descent to converge faster
Let's talk about it ๐งต
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
โช๏ธ Normalization
โช๏ธ Standardization
The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.
Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.
This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.
In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).
More about them:
โช๏ธStandardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
โช๏ธStandardization is robust to outliers.
โช๏ธNormalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.
Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.
https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing
Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.
https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk
Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.
Enable gradient descent to converge faster
๐5โค2
Essential Python and SQL topics for data analysts ๐๐
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Future Trends in Artificial Intelligence ๐๐
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more โค๏ธ
Artificial Intelligence
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more โค๏ธ
Artificial Intelligence
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Transform your skills with these cutting-edge courses by NVIDIA.
Check out the following NVIDIA FREE AI Certification Courses
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Enroll For FREE & Get Certified ๐
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Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
๐2
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
Hi guys,
Join our main channel for all data science resources and free courses ๐๐
https://t.iss.one/datasciencefun
Join our main channel for all data science resources and free courses ๐๐
https://t.iss.one/datasciencefun
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Data Science & Machine Learning
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data
For collaborations: @love_data
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๐ Free useful resources to learn Machine Learning
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
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For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
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