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
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
๐2
50 Linux commands for our day-to-day work:
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ls - List directory contents.2.
pwd - Display current directory path.3.
cd - Change directory.4.
mkdir - Create a new directory.5.
mv - Move or rename files.6.
cp - Copy files.7.
rm - Delete files.8.
touch - Create an empty file.9.
rmdir - Remove directory.10.
cat - Display file content.11.
clear - Clear terminal screen.12.
echo - Output text or data to a file.13.
less - View text files page-by-page.14.
man - Display command manual.15.
sudo - Execute commands with root privileges.16.
top - Show system processes.17.
tar - Archive files into tarball.18.
grep - Search for text within files.19.
head - Display file's beginning lines.20.
tail - Show file's ending lines.21.
diff - Compare two files' content.22.
kill - Terminate processes.23.
jobs - List active jobs.24.
sort - Sort lines of a text file.25.
df - Display disk usage.26.
du - Show file or directory size.27.
zip - Compress files into zip format.28.
unzip - Extract zip archives.29.
ssh - Secure connection between hosts.30.
cal - Display calendar.31.
apt - Manage packages.32.
alias - Create command shortcuts.33.
w - Show current user details.34.
whereis - Locate binaries, sources, and manuals.35.
whatis - Provide command description.36.
useradd - Add a new user.37.
passwd - Change user password.38.
whoami - Display current user name.39.
uptime - Show system runtime.40.
free - Display memory status.41.
history - List command history.42.
uname - Provide system details.43.
ping - Check network connectivity.44.
chmod - Modify file/directory permissions.45.
chown - Change file/directory owner.46.
find - Search for files/directories.47.
locate - Find files quickly.48.
ifconfig - Display network interfaces.49.
ip a - List network interfaces succinctly.50.
finger - Retrieve user information.โค5
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
๐2โค1
Complete Roadmap to learn Generative AI in 2 months ๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
โค3
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
๐ฑ ๐๐ฅ๐๐ ๐๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ฆ๐ธ๐๐ฟ๐ผ๐ฐ๐ธ๐ฒ๐ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
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IBM, one of the biggest tech companies, is offering 5 FREE courses that can seriously upgrade your resume and skills โ without costing you anything.
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Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐2
๐ฐ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ๐ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐
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Harvard and Stanfordโtwo of the most prestigious universities in the worldโare offering FREE AI courses๐จโ๐ป
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๐1
Python Interview Questions for Data/Business Analysts:
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
๐3
๐๐ฅ๐๐ ๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฃ๐ฎ๐๐ต! ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
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โค1๐1
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
๐2
๐๐ฒ๐๐ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐๐
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?๐จโ๐ป
Hereโs the truth: YouTube is packed with goldmine content, and the best part โ itโs all 100% FREE๐ฅ
๐๐ข๐ง๐ค๐:-
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๐ If Youโre Serious About Data Analytics, You Canโt Sleep on These YouTube Channels!
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?๐จโ๐ป
Hereโs the truth: YouTube is packed with goldmine content, and the best part โ itโs all 100% FREE๐ฅ
๐๐ข๐ง๐ค๐:-
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๐ If Youโre Serious About Data Analytics, You Canโt Sleep on These YouTube Channels!
๐1
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
โค2
Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
๐2
๐ง๐๐ฆ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ข๐ป ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐ - ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐๐
Want to know how top companies handle massive amounts of data without losing track? ๐
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Just click and start learning!โ ๏ธ
Want to know how top companies handle massive amounts of data without losing track? ๐
TCS is offering a FREE beginner-friendly course on Master Data Management, and yesโit comes with a certificate! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jGFBw0
Just click and start learning!โ ๏ธ
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๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐ก๐ผ ๐๐ป๐๐ฒ๐๐๐บ๐ฒ๐ป๐ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
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Start today, and youโll thank yourself tomorrow.โ ๏ธ
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
Iโve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects โ absolutely FREE๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lOVqmb
Start today, and youโll thank yourself tomorrow.โ ๏ธ
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