5 Fun AI Agent Projects for Absolute Beginners
๐ฏ 1. Build an AI Calendar Agent (Pure Python)
Easily create your own scheduling agent that reads, plans, and books calendar events with natural language.
๐ Watch here: YouTube
๐ป 2. Coding Agent from Scratch
Learn to code an autonomous coding assistantโno frameworks, just Python logic, loops, and safe tool use.
๐ Watch here: YouTube
๐ง 3. Content Creator Agent (CrewAI + Zapier)
Automate your content pipeline โ from ideation to publishing across platforms using CrewAI workflows.
๐ Watch here: YouTube
๐ 4. Research Agent with Pydantic AI
Turn web searches and PDFs into structured, AI-summarized notes using typed Pydantic outputs.
๐ Watch here: YouTube
๐ 5. Advanced AI Agent with Live Search
Build a graph-based research agent that scrapes, filters, and verifies info from Google, Bing, and Reddit.
๐ Watch here: YouTube
๐ฅ Double Tap โค๏ธ For More
๐ฏ 1. Build an AI Calendar Agent (Pure Python)
Easily create your own scheduling agent that reads, plans, and books calendar events with natural language.
๐ Watch here: YouTube
๐ป 2. Coding Agent from Scratch
Learn to code an autonomous coding assistantโno frameworks, just Python logic, loops, and safe tool use.
๐ Watch here: YouTube
๐ง 3. Content Creator Agent (CrewAI + Zapier)
Automate your content pipeline โ from ideation to publishing across platforms using CrewAI workflows.
๐ Watch here: YouTube
๐ 4. Research Agent with Pydantic AI
Turn web searches and PDFs into structured, AI-summarized notes using typed Pydantic outputs.
๐ Watch here: YouTube
๐ 5. Advanced AI Agent with Live Search
Build a graph-based research agent that scrapes, filters, and verifies info from Google, Bing, and Reddit.
๐ Watch here: YouTube
๐ฅ Double Tap โค๏ธ For More
โค9
โ
Machine Learning Engineer Roadmap
๐ Fundamentals
- Mathematics
โข Linear Algebra
โข Calculus
โข Probability & Statistics
- Programming
โข Python (main)
โข SQL
โข Data Structures & Algorithms
๐ Core Machine Learning
- Supervised Learning
โข Linear & Logistic Regression
โข Decision Trees, Random Forests
โข SVM, KNN, Naive Bayes
- Unsupervised Learning
โข K-Means, DBSCAN
โข PCA, t-SNE
- Model Evaluation
โข Precision, Recall, F1-Score
โข ROC, AUC
โข Cross-validation
๐ง Deep Learning
- Neural Networks
โข Feedforward, CNN, RNN
โข Optimizers, Loss Functions
- Transformers
โข Attention
โข BERT, models
- Frameworks
โข TensorFlow
โข PyTorch
๐ Data Handling
- Data Cleaning & Preprocessing
- Feature Engineering
- Handling Imbalanced Data
๐ Tools & Workflow
- Jupyter, VS Code
- Git & GitHub
- Docker & MLflow
โ๏ธ Deployment
- APIs (Flask/FastAPI)
- CI/CD Basics
- Deployment on AWS / GCP / Azure
๐ Real-World Projects
- End-to-End ML Pipelines
- Model Serving & Monitoring
- Performance Tuning
๐งโ๐ผ Soft Skills & Ethics
- Communication with stakeholders
- Data Privacy & AI Ethics
- Explainable AI
๐ Platforms to Learn
- Kaggle
- Coursera
- fast.ai
- Hugging Face
- Papers with Code
๐ Tap โค๏ธ for more!
๐ Fundamentals
- Mathematics
โข Linear Algebra
โข Calculus
โข Probability & Statistics
- Programming
โข Python (main)
โข SQL
โข Data Structures & Algorithms
๐ Core Machine Learning
- Supervised Learning
โข Linear & Logistic Regression
โข Decision Trees, Random Forests
โข SVM, KNN, Naive Bayes
- Unsupervised Learning
โข K-Means, DBSCAN
โข PCA, t-SNE
- Model Evaluation
โข Precision, Recall, F1-Score
โข ROC, AUC
โข Cross-validation
๐ง Deep Learning
- Neural Networks
โข Feedforward, CNN, RNN
โข Optimizers, Loss Functions
- Transformers
โข Attention
โข BERT, models
- Frameworks
โข TensorFlow
โข PyTorch
๐ Data Handling
- Data Cleaning & Preprocessing
- Feature Engineering
- Handling Imbalanced Data
๐ Tools & Workflow
- Jupyter, VS Code
- Git & GitHub
- Docker & MLflow
โ๏ธ Deployment
- APIs (Flask/FastAPI)
- CI/CD Basics
- Deployment on AWS / GCP / Azure
๐ Real-World Projects
- End-to-End ML Pipelines
- Model Serving & Monitoring
- Performance Tuning
๐งโ๐ผ Soft Skills & Ethics
- Communication with stakeholders
- Data Privacy & AI Ethics
- Explainable AI
๐ Platforms to Learn
- Kaggle
- Coursera
- fast.ai
- Hugging Face
- Papers with Code
๐ Tap โค๏ธ for more!
โค13
Model Optimization Interview Q&A
1/10: Loss Function
Q: What is a loss function and why is it important?
A: Quantifies the difference between predicted and actual values. Guides training.
Examples: MSE (regression), Cross-Entropy (classification)
2/10: Learning Rate
Q: How does learning rate affect training?
A: Controls weight updates.
Too high: Overshooting.
Too low: Slow convergence.
Solution: Schedules, Adam optimizer.
3/10: Overfitting
Q: What is overfitting and how to prevent it?
A: Model learns noise, performs poorly on unseen data.
Prevention: Regularization, Dropout, Early Stopping, Cross-Validation, Data Augmentation.
4/10: Dropout
Q: Explain Dropout.
A: Randomly disables neurons during training to prevent co-adaptation and reduce overfitting.
Rate: 0.2-0.5.
5/10: Batch Normalization
Q: What is Batch Normalization and why is it useful?
A: Normalizes inputs to each layer, stabilizing training.
Benefits: Reduces internal covariate shift, higher learning rates, regularization.
6/10: Optimizer Choice
Q: How to choose the right optimizer?
A: Depends on problem.
SGD: Simple, large datasets.
Adam: Adaptive, faster.
RMSprop: Recurrent networks.
Start with Adam!
7/10: Vanishing/Exploding Gradients
Q: What are vanishing/exploding gradients?
A: During backpropagation in deep networks.
Vanishing: Gradients shrink.
Exploding: Gradients grow uncontrollably.
Solutions: ReLU, gradient clipping, weight initialization.
8/10: Transfer Learning
Q: How does Transfer Learning help?
A: Uses pre-trained models to reduce training time and improve performance.
Fine-tune last layers.
Common in NLP (BERT), CV (ResNet, VGG).
9/10: Early Stopping
Q: What is Early Stopping?
A: Halts training when validation performance stops improving, preventing overfitting.
Monitor validation loss.
10/10: Generalization Evaluation
Q: How to evaluate model generalization?
A: Use unseen test data, cross-validation. Metrics: Accuracy, Precision, Recall, F1-score.
Generalization gap: Training vs. test performance.
Explanation of Formatting Choices:
โข Numbered List: Clearly separates each question and answer.
โข Q&A Format: Simple and direct.
โข Concise Language: Shortened answers to fit within character limits and maintain readability on mobile devices.
โข Keywords/Bullet Points: Uses bullet points for lists to improve clarity.
โข Key Examples: Includes important examples for understanding.
โข Sequential: Keeps the logical flow of the original text.
1/10: Loss Function
Q: What is a loss function and why is it important?
A: Quantifies the difference between predicted and actual values. Guides training.
Examples: MSE (regression), Cross-Entropy (classification)
2/10: Learning Rate
Q: How does learning rate affect training?
A: Controls weight updates.
Too high: Overshooting.
Too low: Slow convergence.
Solution: Schedules, Adam optimizer.
3/10: Overfitting
Q: What is overfitting and how to prevent it?
A: Model learns noise, performs poorly on unseen data.
Prevention: Regularization, Dropout, Early Stopping, Cross-Validation, Data Augmentation.
4/10: Dropout
Q: Explain Dropout.
A: Randomly disables neurons during training to prevent co-adaptation and reduce overfitting.
Rate: 0.2-0.5.
5/10: Batch Normalization
Q: What is Batch Normalization and why is it useful?
A: Normalizes inputs to each layer, stabilizing training.
Benefits: Reduces internal covariate shift, higher learning rates, regularization.
6/10: Optimizer Choice
Q: How to choose the right optimizer?
A: Depends on problem.
SGD: Simple, large datasets.
Adam: Adaptive, faster.
RMSprop: Recurrent networks.
Start with Adam!
7/10: Vanishing/Exploding Gradients
Q: What are vanishing/exploding gradients?
A: During backpropagation in deep networks.
Vanishing: Gradients shrink.
Exploding: Gradients grow uncontrollably.
Solutions: ReLU, gradient clipping, weight initialization.
8/10: Transfer Learning
Q: How does Transfer Learning help?
A: Uses pre-trained models to reduce training time and improve performance.
Fine-tune last layers.
Common in NLP (BERT), CV (ResNet, VGG).
9/10: Early Stopping
Q: What is Early Stopping?
A: Halts training when validation performance stops improving, preventing overfitting.
Monitor validation loss.
10/10: Generalization Evaluation
Q: How to evaluate model generalization?
A: Use unseen test data, cross-validation. Metrics: Accuracy, Precision, Recall, F1-score.
Generalization gap: Training vs. test performance.
Explanation of Formatting Choices:
โข Numbered List: Clearly separates each question and answer.
โข Q&A Format: Simple and direct.
โข Concise Language: Shortened answers to fit within character limits and maintain readability on mobile devices.
โข Keywords/Bullet Points: Uses bullet points for lists to improve clarity.
โข Key Examples: Includes important examples for understanding.
โข Sequential: Keeps the logical flow of the original text.
โค5
If youโre aiming for your first Data Science role, hereโs why you should avoid typical guided projects
Everyoneโs doing โTitanic Survival Predictionโ or โIris Flower Classificationโ these days.
But are these really projects?
Or just red flags?
Remember: Your projects show YOUR skills.
So whatโs wrong with these?
Donโt think from your perspective โ think like a hiring manager.
These projects have millions of tutorials and notebooks online.
Even if half those people actually built them, imagine how many identical projects hiring managers have already seen.
When recruiters sift through hundreds of resumes daily, seeing the same โTitanicโ or โIrisโ projects makes you blend in โ not stand out.
They instantly know these are basic, publicly available projects.
So how can they trust your skills or creativity based on something so common?
What value does a standard Titanic analysis bring to their companyโs unique problems?
Doing these guided projects traps you in a huge pool of competition.
Donโt rely on them for your portfolio or resume.
Guided projects are great for learning and practicing, but you need to build original, meaningful projects that solve real or unique problems to truly impress.
Show your problem-solving, creativity, and ability to handle messy data.
Thatโs what makes hiring managers take notice.
Build projects that speak your skills โ not just follow tutorials. โค๏ธ
Everyoneโs doing โTitanic Survival Predictionโ or โIris Flower Classificationโ these days.
But are these really projects?
Or just red flags?
Remember: Your projects show YOUR skills.
So whatโs wrong with these?
Donโt think from your perspective โ think like a hiring manager.
These projects have millions of tutorials and notebooks online.
Even if half those people actually built them, imagine how many identical projects hiring managers have already seen.
When recruiters sift through hundreds of resumes daily, seeing the same โTitanicโ or โIrisโ projects makes you blend in โ not stand out.
They instantly know these are basic, publicly available projects.
So how can they trust your skills or creativity based on something so common?
What value does a standard Titanic analysis bring to their companyโs unique problems?
Doing these guided projects traps you in a huge pool of competition.
Donโt rely on them for your portfolio or resume.
Guided projects are great for learning and practicing, but you need to build original, meaningful projects that solve real or unique problems to truly impress.
Show your problem-solving, creativity, and ability to handle messy data.
Thatโs what makes hiring managers take notice.
Build projects that speak your skills โ not just follow tutorials. โค๏ธ
โค4
Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.iss.one/machinelearning_deeplearning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.iss.one/machinelearning_deeplearning
Telegram
Artificial Intelligence
๐ฐ Machine Learning & Artificial Intelligence Free Resources
๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data
๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data
โค6
How to get started with data science
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data, from first learning day to a job in tech.
Share this channel link with someone who wants to get into data science and AI but is confused.
๐๐
https://t.iss.one/datasciencefun
Happy learning ๐๐
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data, from first learning day to a job in tech.
Share this channel link with someone who wants to get into data science and AI but is confused.
๐๐
https://t.iss.one/datasciencefun
Happy learning ๐๐
โค5
โ
Must-Know Data Science Concepts for Interviews ๐๐ผ
๐ Statistics & Probability
1. Descriptive vs Inferential statistics
2. Probability distributions (Normal, Binomial, Poisson)
3. Hypothesis testing & p-values
4. Central Limit Theorem
5. Confidence intervals
๐ Data Wrangling & Cleaning
6. Handling missing data
7. Data imputation methods
8. Outlier detection
9. Data transformation & normalization
10. Feature scaling
๐ Machine Learning Basics
11. Supervised vs Unsupervised learning
12. Common algorithms: Linear Regression, Logistic Regression, Decision Trees
13. Overfitting vs Underfitting
14. Bias-Variance tradeoff
15. Evaluation metrics (accuracy, precision, recall, F1-score)
๐ Advanced Machine Learning
16. Random Forests & Gradient Boosting
17. Support Vector Machines
18. Neural Networks basics
19. Dimensionality reduction (PCA, t-SNE)
20. Cross-validation techniques
๐ Python & Libraries
21. NumPy basics (arrays, broadcasting)
22. Pandas (dataframes, indexing)
23. Matplotlib & Seaborn (visualization)
24. Scikit-learn (model building & metrics)
25. Handling large datasets
๐ Data Visualization
26. Types of charts (bar, line, histogram, scatter)
27. Choosing the right visualization
28. Dashboard basics
29. Plotly & interactive viz
30. Storytelling with data
๐ Big Data & Tools
31. Hadoop basics
32. Spark fundamentals
33. SQL queries for data extraction
34. Data warehousing concepts
35. Cloud services (AWS, GCP, Azure)
๐ Deep Learning
36. CNN & RNN overview
37. Backpropagation
38. Transfer learning
39. Frameworks (TensorFlow, PyTorch)
40. Model tuning & optimization
๐ Business & Communication
41. Translating business problems to data tasks
42. KPIs and metrics understanding
43. Presenting insights effectively
44. Storytelling with data
45. Ethics & privacy considerations
๐ Tools & Workflow
46. Git & version control
47. Jupyter notebooks & reproducibility
48. Docker basics
49. Experiment tracking
50. Collaboration in teams
๐ฌ Tap โค๏ธ if this helped you!
๐ Statistics & Probability
1. Descriptive vs Inferential statistics
2. Probability distributions (Normal, Binomial, Poisson)
3. Hypothesis testing & p-values
4. Central Limit Theorem
5. Confidence intervals
๐ Data Wrangling & Cleaning
6. Handling missing data
7. Data imputation methods
8. Outlier detection
9. Data transformation & normalization
10. Feature scaling
๐ Machine Learning Basics
11. Supervised vs Unsupervised learning
12. Common algorithms: Linear Regression, Logistic Regression, Decision Trees
13. Overfitting vs Underfitting
14. Bias-Variance tradeoff
15. Evaluation metrics (accuracy, precision, recall, F1-score)
๐ Advanced Machine Learning
16. Random Forests & Gradient Boosting
17. Support Vector Machines
18. Neural Networks basics
19. Dimensionality reduction (PCA, t-SNE)
20. Cross-validation techniques
๐ Python & Libraries
21. NumPy basics (arrays, broadcasting)
22. Pandas (dataframes, indexing)
23. Matplotlib & Seaborn (visualization)
24. Scikit-learn (model building & metrics)
25. Handling large datasets
๐ Data Visualization
26. Types of charts (bar, line, histogram, scatter)
27. Choosing the right visualization
28. Dashboard basics
29. Plotly & interactive viz
30. Storytelling with data
๐ Big Data & Tools
31. Hadoop basics
32. Spark fundamentals
33. SQL queries for data extraction
34. Data warehousing concepts
35. Cloud services (AWS, GCP, Azure)
๐ Deep Learning
36. CNN & RNN overview
37. Backpropagation
38. Transfer learning
39. Frameworks (TensorFlow, PyTorch)
40. Model tuning & optimization
๐ Business & Communication
41. Translating business problems to data tasks
42. KPIs and metrics understanding
43. Presenting insights effectively
44. Storytelling with data
45. Ethics & privacy considerations
๐ Tools & Workflow
46. Git & version control
47. Jupyter notebooks & reproducibility
48. Docker basics
49. Experiment tracking
50. Collaboration in teams
๐ฌ Tap โค๏ธ if this helped you!
โค25๐ฅฐ1
Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
๐๏ธWeek 1: Foundation of Data Analytics
โพDay 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.
โพDay 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.
โพDay 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.
๐๏ธWeek 2: Intermediate Data Analytics Skills
โพDay 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.
โพDay 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.
โพDay 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.
๐๏ธWeek 3: Advanced Techniques and Tools
โพDay 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.
โพDay 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.
โพDay 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.
๐๏ธWeek 4: Projects and Practice
โพDay 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.
โพDay 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.
โพDay 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.
๐Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science
Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!
โค10๐4๐ฅฐ1
> You don't focus on ML maths
> You don't read technical blogs
> You don't read research papers
> You don't focus on MLOps and only work on jupyter notebooks
> You don't participate in Kaggle contests
> You don't write type-safe Python pipelines
> You don't focus on the "why" of things, you just focus on getting things "done"
> You just talk to ChatGPT for code
And then you say, ML is boring, it's just training a black box and waiting for its output.
ML is boring because you're making it boring. ML is the most interesting field out there right now.
Discoveries, new frontiers, and techniques with solid mathematical intuitions are launched every day.
> You don't read technical blogs
> You don't read research papers
> You don't focus on MLOps and only work on jupyter notebooks
> You don't participate in Kaggle contests
> You don't write type-safe Python pipelines
> You don't focus on the "why" of things, you just focus on getting things "done"
> You just talk to ChatGPT for code
And then you say, ML is boring, it's just training a black box and waiting for its output.
ML is boring because you're making it boring. ML is the most interesting field out there right now.
Discoveries, new frontiers, and techniques with solid mathematical intuitions are launched every day.
๐5โค4๐2
๐ ๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐ป ๐๐/๐๐๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ: ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ
Master the skills ๐๐ฒ๐ฐ๐ต ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ต๐ถ๐ฟ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ: ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ฒ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐ and ๐ฑ๐ฒ๐ฝ๐น๐ผ๐ ๐๐ต๐ฒ๐บ ๐๐ผ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป at scale.
๐๐๐ถ๐น๐ ๐ณ๐ฟ๐ผ๐บ ๐ฟ๐ฒ๐ฎ๐น ๐๐ ๐ท๐ผ๐ฏ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐๐.
โ Fine-tune models with industry tools
โ Deploy on cloud infrastructure
โ 2 portfolio-ready projects
โ Official certification + badge
๐๐ฒ๐ฎ๐ฟ๐ป ๐บ๐ผ๐ฟ๐ฒ & ๐ฒ๐ป๐ฟ๐ผ๐น๐น โคต๏ธ
https://go.readytensor.ai/cert-550-llm-engg-certification
Master the skills ๐๐ฒ๐ฐ๐ต ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ต๐ถ๐ฟ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ: ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ฒ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐ and ๐ฑ๐ฒ๐ฝ๐น๐ผ๐ ๐๐ต๐ฒ๐บ ๐๐ผ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป at scale.
๐๐๐ถ๐น๐ ๐ณ๐ฟ๐ผ๐บ ๐ฟ๐ฒ๐ฎ๐น ๐๐ ๐ท๐ผ๐ฏ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐๐.
โ Fine-tune models with industry tools
โ Deploy on cloud infrastructure
โ 2 portfolio-ready projects
โ Official certification + badge
๐๐ฒ๐ฎ๐ฟ๐ป ๐บ๐ผ๐ฟ๐ฒ & ๐ฒ๐ป๐ฟ๐ผ๐น๐น โคต๏ธ
https://go.readytensor.ai/cert-550-llm-engg-certification
โค6
โ
Must-Know Machine Learning Algorithms ๐ค๐
๐ต Supervised Learning
๐ Classification:
โฆ Naรฏve Bayes
โฆ Logistic Regression
โฆ K-Nearest Neighbor (KNN)
โฆ Random Forest
โฆ Support Vector Machine (SVM)
โฆ Decision Tree
๐ Regression:
โฆ Simple Linear Regression
โฆ Multivariate Regression
โฆ Lasso Regression
๐ก Unsupervised Learning
๐ Clustering:
โฆ K-Means
โฆ DBSCAN
โฆ PCA (Principal Component Analysis)
โฆ ICA (Independent Component Analysis)
๐ Association:
โฆ Frequent Pattern Growth
โฆ Apriori Algorithm
๐ Anomaly Detection:
โฆ Z-score Algorithm
โฆ Isolation Forest
โช Semi-Supervised Learning
โฆ Self-Training
โฆ Co-Training
๐ด Reinforcement Learning
๐ Model-Free:
โฆ Policy Optimization
โฆ Q-Learning
๐ Model-Based:
โฆ Learn the Model
โฆ Given the Model
๐ก Pro Tip: Master at least one algorithm from each category. Understand use cases, tune parameters & evaluate models.
๐ฌ Tap โค๏ธ for more!
๐ต Supervised Learning
๐ Classification:
โฆ Naรฏve Bayes
โฆ Logistic Regression
โฆ K-Nearest Neighbor (KNN)
โฆ Random Forest
โฆ Support Vector Machine (SVM)
โฆ Decision Tree
๐ Regression:
โฆ Simple Linear Regression
โฆ Multivariate Regression
โฆ Lasso Regression
๐ก Unsupervised Learning
๐ Clustering:
โฆ K-Means
โฆ DBSCAN
โฆ PCA (Principal Component Analysis)
โฆ ICA (Independent Component Analysis)
๐ Association:
โฆ Frequent Pattern Growth
โฆ Apriori Algorithm
๐ Anomaly Detection:
โฆ Z-score Algorithm
โฆ Isolation Forest
โช Semi-Supervised Learning
โฆ Self-Training
โฆ Co-Training
๐ด Reinforcement Learning
๐ Model-Free:
โฆ Policy Optimization
โฆ Q-Learning
๐ Model-Based:
โฆ Learn the Model
โฆ Given the Model
๐ก Pro Tip: Master at least one algorithm from each category. Understand use cases, tune parameters & evaluate models.
๐ฌ Tap โค๏ธ for more!
โค18
๐ค Top AI Technologies & Their Real-World Uses ๐๐ก
๐น Machine Learning (ML)
1. Predictive Analytics
2. Fraud Detection
3. Product Recommendations
4. Stock Market Forecasting
5. Image & Speech Recognition
6. Spam Filtering
7. Autonomous Vehicles
8. Sentiment Analysis
๐น Natural Language Processing (NLP)
1. Chatbots & Virtual Assistants
2. Language Translation
3. Text Summarization
4. Voice Commands
5. Sentiment Analysis
6. Email Categorization
7. Resume Screening
8. Customer Support Automation
๐น Computer Vision
1. Facial Recognition
2. Object Detection
3. Medical Imaging
4. Traffic Monitoring
5. AR/VR Integration
6. Retail Shelf Analysis
7. License Plate Recognition
8. Surveillance Systems
๐น Robotics
1. Industrial Automation
2. Warehouse Management
3. Medical Surgery
4. Agriculture Robotics
5. Military Drones
6. Delivery Robots
7. Disaster Response
8. Home Cleaning Bots
๐น Generative AI
1. Text Generation (e.g. Chat)
2. Image Generation (e.g. DALLยทE, Midjourney)
3. Music & Voice Synthesis
4. Code Generation
5. Video Creation
6. Digital Art & NFTs
7. Content Marketing
8. Personalized Learning
๐น Reinforcement Learning
1. Game AI (Chess, Go, Dota)
2. Robotics Navigation
3. Portfolio Management
4. Smart Traffic Systems
5. Personalized Ads
6. Drone Flight Control
7. Warehouse Automation
8. Energy Optimization
๐ Tap โค๏ธ for more! .
๐น Machine Learning (ML)
1. Predictive Analytics
2. Fraud Detection
3. Product Recommendations
4. Stock Market Forecasting
5. Image & Speech Recognition
6. Spam Filtering
7. Autonomous Vehicles
8. Sentiment Analysis
๐น Natural Language Processing (NLP)
1. Chatbots & Virtual Assistants
2. Language Translation
3. Text Summarization
4. Voice Commands
5. Sentiment Analysis
6. Email Categorization
7. Resume Screening
8. Customer Support Automation
๐น Computer Vision
1. Facial Recognition
2. Object Detection
3. Medical Imaging
4. Traffic Monitoring
5. AR/VR Integration
6. Retail Shelf Analysis
7. License Plate Recognition
8. Surveillance Systems
๐น Robotics
1. Industrial Automation
2. Warehouse Management
3. Medical Surgery
4. Agriculture Robotics
5. Military Drones
6. Delivery Robots
7. Disaster Response
8. Home Cleaning Bots
๐น Generative AI
1. Text Generation (e.g. Chat)
2. Image Generation (e.g. DALLยทE, Midjourney)
3. Music & Voice Synthesis
4. Code Generation
5. Video Creation
6. Digital Art & NFTs
7. Content Marketing
8. Personalized Learning
๐น Reinforcement Learning
1. Game AI (Chess, Go, Dota)
2. Robotics Navigation
3. Portfolio Management
4. Smart Traffic Systems
5. Personalized Ads
6. Drone Flight Control
7. Warehouse Automation
8. Energy Optimization
๐ Tap โค๏ธ for more! .
โค22๐1๐1
โ
25 AI & Machine Learning Abbreviations You Should Know ๐ค๐ง
1๏ธโฃ AI โ Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.
2๏ธโฃ ML โ Machine Learning: AI subset where models learn from data without explicit programmingโthink predictive analytics.
3๏ธโฃ DL โ Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.
4๏ธโฃ NLP โ Natural Language Processing: Handling human language for chatbots or sentiment analysis.
5๏ธโฃ CV โ Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.
6๏ธโฃ ANN โ Artificial Neural Network: Brain-inspired structures for pattern detection in data.
7๏ธโฃ CNN โ Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.
8๏ธโฃ RNN โ Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.
9๏ธโฃ GAN โ Generative Adversarial Network: Two nets competing to create realistic data, like fake images.
๐ RL โ Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.
1๏ธโฃ1๏ธโฃ SVM โ Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.
1๏ธโฃ2๏ธโฃ KNN โ K-Nearest Neighbors: Simple ML for grouping based on closest data pointsโlazy learner!
1๏ธโฃ3๏ธโฃ PCA โ Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.
1๏ธโฃ4๏ธโฃ API โ Application Programming Interface: Bridges software, like calling OpenAI's models in your app.
1๏ธโฃ5๏ธโฃ GPU โ Graphics Processing Unit: Hardware accelerating parallel computations for training big models.
1๏ธโฃ6๏ธโฃ TPU โ Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.
1๏ธโฃ7๏ธโฃ IoT โ Internet of Things: Networked devices collecting data, feeding into AI for smart homes.
1๏ธโฃ8๏ธโฃ BERT โ Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.
1๏ธโฃ9๏ธโฃ LSTM โ Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.
2๏ธโฃ0๏ธโฃ ASR โ Automatic Speech Recognition: Converts voice to text, like Siri or transcription tools.
2๏ธโฃ1๏ธโฃ OCR โ Optical Character Recognition: Extracts text from images, e.g., scanning docs.
2๏ธโฃ2๏ธโฃ Q-Learning โ Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.
2๏ธโฃ3๏ธโฃ MLP โ Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.
2๏ธโฃ4๏ธโฃ LLM โ Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).
2๏ธโฃ5๏ธโฃ TF-IDF โ Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.
๐ฌ Tap โค๏ธ for more!
1๏ธโฃ AI โ Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.
2๏ธโฃ ML โ Machine Learning: AI subset where models learn from data without explicit programmingโthink predictive analytics.
3๏ธโฃ DL โ Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.
4๏ธโฃ NLP โ Natural Language Processing: Handling human language for chatbots or sentiment analysis.
5๏ธโฃ CV โ Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.
6๏ธโฃ ANN โ Artificial Neural Network: Brain-inspired structures for pattern detection in data.
7๏ธโฃ CNN โ Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.
8๏ธโฃ RNN โ Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.
9๏ธโฃ GAN โ Generative Adversarial Network: Two nets competing to create realistic data, like fake images.
๐ RL โ Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.
1๏ธโฃ1๏ธโฃ SVM โ Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.
1๏ธโฃ2๏ธโฃ KNN โ K-Nearest Neighbors: Simple ML for grouping based on closest data pointsโlazy learner!
1๏ธโฃ3๏ธโฃ PCA โ Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.
1๏ธโฃ4๏ธโฃ API โ Application Programming Interface: Bridges software, like calling OpenAI's models in your app.
1๏ธโฃ5๏ธโฃ GPU โ Graphics Processing Unit: Hardware accelerating parallel computations for training big models.
1๏ธโฃ6๏ธโฃ TPU โ Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.
1๏ธโฃ7๏ธโฃ IoT โ Internet of Things: Networked devices collecting data, feeding into AI for smart homes.
1๏ธโฃ8๏ธโฃ BERT โ Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.
1๏ธโฃ9๏ธโฃ LSTM โ Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.
2๏ธโฃ0๏ธโฃ ASR โ Automatic Speech Recognition: Converts voice to text, like Siri or transcription tools.
2๏ธโฃ1๏ธโฃ OCR โ Optical Character Recognition: Extracts text from images, e.g., scanning docs.
2๏ธโฃ2๏ธโฃ Q-Learning โ Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.
2๏ธโฃ3๏ธโฃ MLP โ Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.
2๏ธโฃ4๏ธโฃ LLM โ Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).
2๏ธโฃ5๏ธโฃ TF-IDF โ Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.
๐ฌ Tap โค๏ธ for more!
โค15๐2
๐ Machine Learning Cheat Sheet ๐
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
๐ Dive into Machine Learning and transform data into insights! ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
๐ Dive into Machine Learning and transform data into insights! ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค7
The 5 FREE Must-Read Books for Every AI Engineer
1. Practical Deep Learning
A hands-on course using Python, PyTorch, and fastai to build, train, and deploy real-world deep learning models through interactive notebooks and applied projects.
2. Neural Networks and Deep Learning
An intuitive and code-rich introduction to building and training deep neural networks from scratch, covering key topics like backpropagation, regularization, and hyperparameter tuning.
3. Deep Learning
A comprehensive, math-heavy reference on modern deep learningโcovering theory, core architectures, optimization, and advanced concepts like generative and probabilistic models.
4. Artificial Intelligence: Foundations of Computational Agents
Explains AI through computational agents that learn, plan, and act, blending theory, Python examples, and ethical considerations into a balanced and modern overview.
5. Ethical Artificial Intelligence
Explores how to design safe AI systems by aligning them with human values and preventing issues like self-delusion, reward hacking, and unintended harmful behavior
Double Tap โค๏ธ For More
1. Practical Deep Learning
A hands-on course using Python, PyTorch, and fastai to build, train, and deploy real-world deep learning models through interactive notebooks and applied projects.
2. Neural Networks and Deep Learning
An intuitive and code-rich introduction to building and training deep neural networks from scratch, covering key topics like backpropagation, regularization, and hyperparameter tuning.
3. Deep Learning
A comprehensive, math-heavy reference on modern deep learningโcovering theory, core architectures, optimization, and advanced concepts like generative and probabilistic models.
4. Artificial Intelligence: Foundations of Computational Agents
Explains AI through computational agents that learn, plan, and act, blending theory, Python examples, and ethical considerations into a balanced and modern overview.
5. Ethical Artificial Intelligence
Explores how to design safe AI systems by aligning them with human values and preventing issues like self-delusion, reward hacking, and unintended harmful behavior
Double Tap โค๏ธ For More
โค11๐4๐1
Stanfordโs Machine Learning - by Andrew Ng
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras โ
โค8๐4๐2
Useful WhatsApp channels to learn AI Tools ๐ค
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
React โค๏ธ for more
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
React โค๏ธ for more
โค15๐1