FIVE TOP IMAGE RECOGNITION SOFTWARE 2024
Image recognition software is a computer program that uses deep learning algorithms and AI to identify objects, scenes, people, text, and activities in images and videos. The software works by extracting pixel features from an image, preparing labeled images for training, training the model to recognize images, and then using the trained model to identify objects in new images.
1. Meltwater Image Search: Meltwater's image recognition software offers social media monitoring capabilities with AI-powered computer vision models. It can search for images in non-verbal and non-textual content, detect demographics, celebrities, scenes, objects, and visual emotions. It also includes features like optical character recognition (OCR) and logo detection.
2. Google Reverse Image Search: Google's Reverse Image Search allows users to find more information about images by uploading them. It can identify objects in the image, provide similar images, and show websites with the same or similar images.
3. Clarifai: Clarifai's AI-powered computer vision software enables processing of images, videos, texts, and audio files. It can filter unwanted content, recommend relevant products, and manage unstructured data. Customizable AI models can be created for specific use cases.
4. Imagga: Imagga offers image recognition tools for sorting, organizing, and displaying images based on tags or categories. Its powerful API enables features such as product discoverability, facial recognition, and automated thumbnail generation.
5. Amazon Rekognition: Amazon Rekognition is a user-friendly image recognition software that provides insights on still images and videos. It offers features like activity recognition, face analysis, content moderation for unsafe and inappropriate content, and text detection for street names, image captions, and license plate numbers.
Image recognition software is a computer program that uses deep learning algorithms and AI to identify objects, scenes, people, text, and activities in images and videos. The software works by extracting pixel features from an image, preparing labeled images for training, training the model to recognize images, and then using the trained model to identify objects in new images.
1. Meltwater Image Search: Meltwater's image recognition software offers social media monitoring capabilities with AI-powered computer vision models. It can search for images in non-verbal and non-textual content, detect demographics, celebrities, scenes, objects, and visual emotions. It also includes features like optical character recognition (OCR) and logo detection.
2. Google Reverse Image Search: Google's Reverse Image Search allows users to find more information about images by uploading them. It can identify objects in the image, provide similar images, and show websites with the same or similar images.
3. Clarifai: Clarifai's AI-powered computer vision software enables processing of images, videos, texts, and audio files. It can filter unwanted content, recommend relevant products, and manage unstructured data. Customizable AI models can be created for specific use cases.
4. Imagga: Imagga offers image recognition tools for sorting, organizing, and displaying images based on tags or categories. Its powerful API enables features such as product discoverability, facial recognition, and automated thumbnail generation.
5. Amazon Rekognition: Amazon Rekognition is a user-friendly image recognition software that provides insights on still images and videos. It offers features like activity recognition, face analysis, content moderation for unsafe and inappropriate content, and text detection for street names, image captions, and license plate numbers.
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Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
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Difference between linear regression and logistic regression 👇👇
Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.
Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.
Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
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❤1
100 Days of Data Science Roadmap
👇👇
https://www.linkedin.com/posts/sql-analysts_100-days-of-data-science-roadmap-2024-activity-7199265302499483648-FDq3
Like for more
👇👇
https://www.linkedin.com/posts/sql-analysts_100-days-of-data-science-roadmap-2024-activity-7199265302499483648-FDq3
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How to enter into Data Science
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
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Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
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10 commonly asked data science interview questions along with their answers
1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
👍34
Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence
1. Computer vision
2. Natural language processing.
I outlined a roadmap for computer vision I believe many beginners will find helpful.
Artificial Intelligence
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What is PCA
PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:
🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.
🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.
🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.
🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.
🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.
🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.
🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.
Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.
PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:
🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.
🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.
🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.
🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.
🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.
🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.
🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.
Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.
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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 🧵
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ 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:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization 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 🧵
There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ 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:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization 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
Google
DS - Feature Scaling.ipynb
Colaboratory notebook
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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 👍❤️ 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 👍❤️ if you guys want me to share more of this content...
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Stop wasting your 20s partying, dating, and being in a comfort zone.
Here are 15 habits that will transform your life in 90 days:
👇👇
Health Fitness Tips
Here are 15 habits that will transform your life in 90 days:
👇👇
Health Fitness Tips
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If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving
And building as much as possible.
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