โญ๏ธ Benefits of Generative AI
Generative AI is one of the outstanding technologies today with many practical benefits such as:
Create Unique Content: Innovative AI algorithms are capable of generating new and unique content such as images, videos, and text that are difficult to distinguish from human-generated content. This benefits many applications such as entertainment, advertising, and creative arts.
Enhancing AI System Efficiency: Generative AI can be applied to improve the performance and accuracy of current AI systems, such as natural language processing and computer vision. For example, general AI algorithms can generate synthetic data to train and test other AI algorithms.
Discovering New Data: Innovative AI has the ability to explore and analyze complex data in new ways, helping businesses and researchers learn about hidden patterns and trends that raw data can reveal. not shown clearly.
Process Automation and Acceleration: Generative AI algorithms can help automate and accelerate a variety of tasks and processes. This saves businesses and organizations time and resources, while increasing productivity.
Generative AI is one of the outstanding technologies today with many practical benefits such as:
Create Unique Content: Innovative AI algorithms are capable of generating new and unique content such as images, videos, and text that are difficult to distinguish from human-generated content. This benefits many applications such as entertainment, advertising, and creative arts.
Enhancing AI System Efficiency: Generative AI can be applied to improve the performance and accuracy of current AI systems, such as natural language processing and computer vision. For example, general AI algorithms can generate synthetic data to train and test other AI algorithms.
Discovering New Data: Innovative AI has the ability to explore and analyze complex data in new ways, helping businesses and researchers learn about hidden patterns and trends that raw data can reveal. not shown clearly.
Process Automation and Acceleration: Generative AI algorithms can help automate and accelerate a variety of tasks and processes. This saves businesses and organizations time and resources, while increasing productivity.
๐2
๐ฅ Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1๏ธโฃ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2๏ธโฃ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3๏ธโฃ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
โญ๏ธ 41.4k stars on Github
๐ https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1๏ธโฃ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2๏ธโฃ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3๏ธโฃ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
โญ๏ธ 41.4k stars on Github
๐ https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
โค2
๐ฐ Machine Learning Roadmap for Beginners 2025
โโโ ๐ง What is Machine Learning?
โโโ ๐งช ML vs AI vs Deep Learning
โโโ ๐ข Math Foundation (Linear Algebra, Calculus, Stats Basics)
โโโ ๐ Python Libraries (NumPy, Pandas, Scikit-learn)
โโโ ๐ Data Preprocessing & Cleaning
โโโ ๐ Feature Selection & Engineering
โโโ ๐งญ Supervised Learning (Regression, Classification)
โโโ ๐งฑ Unsupervised Learning (Clustering, Dimensionality Reduction)
โโโ ๐น Model Evaluation (Confusion Matrix, ROC, AUC)
โโโ โ๏ธ Model Tuning (Hyperparameter Tuning, Grid Search)
โโโ ๐งฐ Ensemble Methods (Bagging, Boosting, Random Forests)
โโโ ๐ฎ Introduction to Neural Networks
โโโ ๐ Overfitting vs Underfitting
โโโ ๐ Model Deployment (Streamlit, Flask, FastAPI Basics)
โโโ ๐งช ML Projects (Classification, Forecasting, Recommender)
โโโ ๐ ML Competitions (Kaggle, Hackathons)
Like for the detailed explanation โค๏ธ
#machinelearning
โโโ ๐ง What is Machine Learning?
โโโ ๐งช ML vs AI vs Deep Learning
โโโ ๐ข Math Foundation (Linear Algebra, Calculus, Stats Basics)
โโโ ๐ Python Libraries (NumPy, Pandas, Scikit-learn)
โโโ ๐ Data Preprocessing & Cleaning
โโโ ๐ Feature Selection & Engineering
โโโ ๐งญ Supervised Learning (Regression, Classification)
โโโ ๐งฑ Unsupervised Learning (Clustering, Dimensionality Reduction)
โโโ ๐น Model Evaluation (Confusion Matrix, ROC, AUC)
โโโ โ๏ธ Model Tuning (Hyperparameter Tuning, Grid Search)
โโโ ๐งฐ Ensemble Methods (Bagging, Boosting, Random Forests)
โโโ ๐ฎ Introduction to Neural Networks
โโโ ๐ Overfitting vs Underfitting
โโโ ๐ Model Deployment (Streamlit, Flask, FastAPI Basics)
โโโ ๐งช ML Projects (Classification, Forecasting, Recommender)
โโโ ๐ ML Competitions (Kaggle, Hackathons)
Like for the detailed explanation โค๏ธ
#machinelearning
๐4
Essential Skills to Master for Using Generative AI
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
๐2
Top 10 machine Learning algorithms ๐๐
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
๐4
1700001429173.pdf
427.3 KB
Top Python libraries for generative AI
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Programming Practice Python 2023.pdf
5.4 MB
Programming Practice Python
Like for more
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Artificial Intelligence for Learning.pdf
2.8 MB
Artificial Intelligence for Learning
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Masato_Hagiwara_Real_World_Natural_Language_Processing_Practical.pdf
11.5 MB
Real-World Natural Language Processing
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Masato Hagiwara, 2021
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5 Free NLP Courses Iโd Recommend for 2025
1. NLP in Python: ๐ Course
Learn fundamental NLP techniques using Python with hands-on projects.
2. AI Chatbots (No Code): ๐ Course
Build AI-powered chatbots without programming in this IBM course.
3. Data Science Basics: ๐ Course
Beginner-friendly tutorials on data analysis, mining, and modeling.
4. NLP on Google Cloud: ๐ Course
Advanced NLP with TensorFlow and Google Cloud tools for professionals.
5. NLP Specialization: ๐ Course
All the best ๐๐
1. NLP in Python: ๐ Course
Learn fundamental NLP techniques using Python with hands-on projects.
2. AI Chatbots (No Code): ๐ Course
Build AI-powered chatbots without programming in this IBM course.
3. Data Science Basics: ๐ Course
Beginner-friendly tutorials on data analysis, mining, and modeling.
4. NLP on Google Cloud: ๐ Course
Advanced NLP with TensorFlow and Google Cloud tools for professionals.
5. NLP Specialization: ๐ Course
All the best ๐๐
๐2
Here's a step-by-step beginner's roadmap for learning machine learning:๐ช๐
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
๐ง ๐Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
๐ง ๐Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
๐2
Generative AI is a multi-billion dollar opportunity!
There will be some winners and losers emerging directly or indirectly impacted by Gen AI ๐ ๐น
But, how to leverage it for the business impact? What are the right steps?
โ๏ธClearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
๐ Uses public models with minimal customization at a lower cost.
๐ค Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
๐Develops a unique foundation model for a specific business case, which requires substantial investment.
โ๏ธDevelop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
โ๏ธQuickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
โ๏ธIntegrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
โ๏ธCreate a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
โ๏ธUse existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
โ๏ธUpgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
โ๏ธDevelop a data architecture that can process both structured and unstructured data.
โ๏ธEstablish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
โ๏ธUpskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
โ๏ธAssess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
โ๏ธFor many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
There will be some winners and losers emerging directly or indirectly impacted by Gen AI ๐ ๐น
But, how to leverage it for the business impact? What are the right steps?
โ๏ธClearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
๐ Uses public models with minimal customization at a lower cost.
๐ค Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
๐Develops a unique foundation model for a specific business case, which requires substantial investment.
โ๏ธDevelop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
โ๏ธQuickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
โ๏ธIntegrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
โ๏ธCreate a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
โ๏ธUse existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
โ๏ธUpgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
โ๏ธDevelop a data architecture that can process both structured and unstructured data.
โ๏ธEstablish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
โ๏ธUpskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
โ๏ธAssess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
โ๏ธFor many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
๐2
๐๐จ๐ฐ ๐ญ๐จ ๐๐๐ ๐ข๐ง ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐ ๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐๐
โช๏ธ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development.
โช๏ธ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications.
โช๏ธ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs.
โช๏ธ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks.
๐น ๐๐๐ฏ๐๐ฅ ๐: ๐๐๐ฏ๐๐ง๐๐๐ ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐ข๐ง ๐๐ ๐๐ ๐๐ง๐ญ๐ฌ
โช๏ธ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications.
โช๏ธ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs.
โช๏ธ Introduction to AI Agents: Get an overview of AI agentsโautonomous entities that use AI to perform tasks or solve problems.
โช๏ธ Agentic Frameworks: Explore popular tools like LangChain or OpenAIโs API to build and manage AI agents.
โช๏ธ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent.
โช๏ธ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops.
โช๏ธ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency.
โช๏ธ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents.
โช๏ธ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently.
โช๏ธ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively.
Join for more AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
๐1
Tech Stack Roadmaps by Career Path ๐ฃ๏ธ
What to learn depending on the job youโre aiming for ๐
1. Frontend Developer
โฏ HTML, CSS, JavaScript
โฏ Git & GitHub
โฏ React / Vue / Angular
โฏ Responsive Design
โฏ Tailwind / Bootstrap
โฏ REST APIs
โฏ TypeScript (Bonus)
โฏ Testing (Jest, Cypress)
โฏ Deployment (Netlify, Vercel)
2. Backend Developer
โฏ Any language (Node.js, Python, Java, Go)
โฏ Git & GitHub
โฏ REST APIs & JSON
โฏ Databases (SQL & NoSQL)
โฏ Authentication & Security
โฏ Docker & CI/CD Basics
โฏ Unit Testing
โฏ Frameworks (Express, Django, Spring Boot)
โฏ Deployment (Render, Railway, AWS)
3. Full-Stack Developer
โฏ Everything from Frontend + Backend
โฏ MVC Architecture
โฏ API Integration
โฏ State Management (Redux, Context API)
โฏ Deployment Pipelines
โฏ Git Workflows (PRs, Branching)
4. Data Analyst
โฏ Excel, SQL
โฏ Python (Pandas, NumPy)
โฏ Data Visualization (Matplotlib, Seaborn)
โฏ Power BI / Tableau
โฏ Statistics & EDA
โฏ Jupyter Notebooks
โฏ Business Acumen
5. DevOps Engineer
โฏ Linux & Shell Scripting
โฏ Git & GitHub
โฏ Docker & Kubernetes
โฏ CI/CD Tools (Jenkins, GitHub Actions)
โฏ Cloud (AWS, GCP, Azure)
โฏ Monitoring (Prometheus, Grafana)
โฏ IaC (Terraform, Ansible)
6. Machine Learning Engineer
โฏ Python + Math (Linear Algebra, Stats)
โฏ Scikit-learn, Pandas, NumPy
โฏ Deep Learning (TensorFlow/PyTorch)
โฏ ML Lifecycle (Train, Tune, Deploy)
โฏ Model Evaluation
โฏ MLOps (MLflow, Docker, FastAPI)
React with โค๏ธ if you found this helpful โ content like this is rare to find on the internet!
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
What to learn depending on the job youโre aiming for ๐
1. Frontend Developer
โฏ HTML, CSS, JavaScript
โฏ Git & GitHub
โฏ React / Vue / Angular
โฏ Responsive Design
โฏ Tailwind / Bootstrap
โฏ REST APIs
โฏ TypeScript (Bonus)
โฏ Testing (Jest, Cypress)
โฏ Deployment (Netlify, Vercel)
2. Backend Developer
โฏ Any language (Node.js, Python, Java, Go)
โฏ Git & GitHub
โฏ REST APIs & JSON
โฏ Databases (SQL & NoSQL)
โฏ Authentication & Security
โฏ Docker & CI/CD Basics
โฏ Unit Testing
โฏ Frameworks (Express, Django, Spring Boot)
โฏ Deployment (Render, Railway, AWS)
3. Full-Stack Developer
โฏ Everything from Frontend + Backend
โฏ MVC Architecture
โฏ API Integration
โฏ State Management (Redux, Context API)
โฏ Deployment Pipelines
โฏ Git Workflows (PRs, Branching)
4. Data Analyst
โฏ Excel, SQL
โฏ Python (Pandas, NumPy)
โฏ Data Visualization (Matplotlib, Seaborn)
โฏ Power BI / Tableau
โฏ Statistics & EDA
โฏ Jupyter Notebooks
โฏ Business Acumen
5. DevOps Engineer
โฏ Linux & Shell Scripting
โฏ Git & GitHub
โฏ Docker & Kubernetes
โฏ CI/CD Tools (Jenkins, GitHub Actions)
โฏ Cloud (AWS, GCP, Azure)
โฏ Monitoring (Prometheus, Grafana)
โฏ IaC (Terraform, Ansible)
6. Machine Learning Engineer
โฏ Python + Math (Linear Algebra, Stats)
โฏ Scikit-learn, Pandas, NumPy
โฏ Deep Learning (TensorFlow/PyTorch)
โฏ ML Lifecycle (Train, Tune, Deploy)
โฏ Model Evaluation
โฏ MLOps (MLflow, Docker, FastAPI)
React with โค๏ธ if you found this helpful โ content like this is rare to find on the internet!
Credits: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
โค5๐2