Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
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Step-by-Step Approach to Learn Generative AI
➊ Learn AI & Deep Learning Basics → Neural Networks, Supervised vs. Unsupervised Learning
↓
➋ Master Python & Essential Libraries → NumPy, Pandas, TensorFlow, PyTorch
↓
➌ Understand Neural Networks → Activation Functions, Backpropagation, Optimizers
↓
➍ Learn GANs (Generative Adversarial Networks) → Generator vs. Discriminator, Training Stability
↓
➎ Explore Variational Autoencoders (VAEs) → Latent Space, KL Divergence
↓
➏ NLP & Large Language Models (LLMs) → Transformers, BERT, GPT, Tokenization
↓
➐ Text-to-Image & Multimodal Models → Stable Diffusion, DALL·E, CLIP
↓
➑ Fine-tuning & Deployment → Custom Model Training, APIs, Model Optimization
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
➊ Learn AI & Deep Learning Basics → Neural Networks, Supervised vs. Unsupervised Learning
↓
➋ Master Python & Essential Libraries → NumPy, Pandas, TensorFlow, PyTorch
↓
➌ Understand Neural Networks → Activation Functions, Backpropagation, Optimizers
↓
➍ Learn GANs (Generative Adversarial Networks) → Generator vs. Discriminator, Training Stability
↓
➎ Explore Variational Autoencoders (VAEs) → Latent Space, KL Divergence
↓
➏ NLP & Large Language Models (LLMs) → Transformers, BERT, GPT, Tokenization
↓
➐ Text-to-Image & Multimodal Models → Stable Diffusion, DALL·E, CLIP
↓
➑ Fine-tuning & Deployment → Custom Model Training, APIs, Model Optimization
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) 🎓
Here are 8 FREE courses to master AI in 2024:
1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118
2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
Here are 8 FREE courses to master AI in 2024:
1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118
2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
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Startup ideas with Generative AI
👇👇
1. Personalized wellness AI: Develop an AI platform that analyzes users' lifestyle habits, health data, and preferences to provide personalized recommendations for improving overall wellness.
2. AI-powered virtual assistant for small businesses: Create a virtual assistant that uses AI to help small business owners manage tasks such as scheduling appointments, sending reminders, and handling customer inquiries.
3. AI-powered content creation tool: Develop an AI tool that can generate high-quality written content, such as blog posts or social media updates, based on a user's input and preferences.
4. AI-driven personalized shopping experience: Build an AI platform that analyzes users' browsing history, purchase behavior, and preferences to recommend personalized product suggestions and discounts.
5. AI-powered mental health support platform: Create an AI-driven platform that provides personalized mental health support, including therapy sessions, coping strategies, and resources for managing stress and anxiety.
6. AI-driven sustainability platform: Develop an AI platform that helps businesses and individuals track their carbon footprint, set sustainability goals, and receive personalized recommendations for reducing environmental impact.
7. AI-powered language learning platform: Build an AI platform that uses natural language processing and machine learning to personalize language learning experiences for users, helping them improve their proficiency in a new language.
8. AI-driven financial planning tool: Create an AI tool that analyzes users' financial data, spending habits, and goals to provide personalized recommendations for budgeting, saving, and investing.
9. AI-powered talent recruitment platform: Develop an AI platform that uses data analytics and machine learning to match job seekers with employers based on their skills, experience, and preferences.
10. AI-driven personalized travel planning platform: Build an AI platform that analyzes users' travel preferences, budget, and interests to recommend personalized travel itineraries, accommodations, and activities.
👇👇
1. Personalized wellness AI: Develop an AI platform that analyzes users' lifestyle habits, health data, and preferences to provide personalized recommendations for improving overall wellness.
2. AI-powered virtual assistant for small businesses: Create a virtual assistant that uses AI to help small business owners manage tasks such as scheduling appointments, sending reminders, and handling customer inquiries.
3. AI-powered content creation tool: Develop an AI tool that can generate high-quality written content, such as blog posts or social media updates, based on a user's input and preferences.
4. AI-driven personalized shopping experience: Build an AI platform that analyzes users' browsing history, purchase behavior, and preferences to recommend personalized product suggestions and discounts.
5. AI-powered mental health support platform: Create an AI-driven platform that provides personalized mental health support, including therapy sessions, coping strategies, and resources for managing stress and anxiety.
6. AI-driven sustainability platform: Develop an AI platform that helps businesses and individuals track their carbon footprint, set sustainability goals, and receive personalized recommendations for reducing environmental impact.
7. AI-powered language learning platform: Build an AI platform that uses natural language processing and machine learning to personalize language learning experiences for users, helping them improve their proficiency in a new language.
8. AI-driven financial planning tool: Create an AI tool that analyzes users' financial data, spending habits, and goals to provide personalized recommendations for budgeting, saving, and investing.
9. AI-powered talent recruitment platform: Develop an AI platform that uses data analytics and machine learning to match job seekers with employers based on their skills, experience, and preferences.
10. AI-driven personalized travel planning platform: Build an AI platform that analyzes users' travel preferences, budget, and interests to recommend personalized travel itineraries, accommodations, and activities.
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🗂 A collection of the good Gen AI free courses
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
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LLM Projects to Boost Your Resume
🔹 Document Analysis using LLMs
Extract insights from unstructured documents using LLMs.
🔹 RAG Pipeline for LLMs
Reduce hallucinations in LLMs with a scalable RAG system.
🔹 AI Image Caption System
Generate high-quality captions for images using AI.
🔹 Train an LLM from Scratch
Build and train a mini LLM using PyTorch or TensorFlow.
🔹 Document Analysis using LLMs
Extract insights from unstructured documents using LLMs.
🔹 RAG Pipeline for LLMs
Reduce hallucinations in LLMs with a scalable RAG system.
🔹 AI Image Caption System
Generate high-quality captions for images using AI.
🔹 Train an LLM from Scratch
Build and train a mini LLM using PyTorch or TensorFlow.
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Generative AI vs Predictive AI :
Generative AI is all about creation. It’s designed to generate new content—like text, images, code, music, or even videos. Think of tools like ChatGPT, DALL·E, or GitHub Copilot. These models learn patterns from massive datasets and use them to produce something new that didn’t exist before.
Predictive AI, on the other hand, is focused on forecasting. It uses historical data to predict future outcomes—like predicting customer churn, stock prices, or product demand.
You’ll often see this in traditional machine learning models such as regression, classification, or time-series forecasting.
In simple terms:
Generative AI = “Create something new.”
Predictive AI = “Tell me what’s likely to happen.”
#genai
Generative AI is all about creation. It’s designed to generate new content—like text, images, code, music, or even videos. Think of tools like ChatGPT, DALL·E, or GitHub Copilot. These models learn patterns from massive datasets and use them to produce something new that didn’t exist before.
Predictive AI, on the other hand, is focused on forecasting. It uses historical data to predict future outcomes—like predicting customer churn, stock prices, or product demand.
You’ll often see this in traditional machine learning models such as regression, classification, or time-series forecasting.
In simple terms:
Generative AI = “Create something new.”
Predictive AI = “Tell me what’s likely to happen.”
#genai
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Generative AI vs Traditional Machine Learning:
Generative AI is a newer branch of AI focused on creating data—like writing text, generating art, producing music, or even designing websites. It uses advanced models like transformers, GANs (Generative Adversarial Networks), and diffusion models to understand patterns and generate new outputs. Examples include ChatGPT, Midjourney, and RunwayML.
Traditional Machine Learning, on the other hand, is more about analyzing and predicting. It involves algorithms like decision trees, linear regression, logistic regression, and k-means clustering that learn from data to make predictions or classify things. You feed it data, and it tells you something about it—like whether an email is spam, or what your next sales numbers might be.
To put it simply:
Generative AI = “Make something new from what you’ve learned.”
Traditional ML = “Understand patterns and make decisions based on them.”
#genai
Generative AI is a newer branch of AI focused on creating data—like writing text, generating art, producing music, or even designing websites. It uses advanced models like transformers, GANs (Generative Adversarial Networks), and diffusion models to understand patterns and generate new outputs. Examples include ChatGPT, Midjourney, and RunwayML.
Traditional Machine Learning, on the other hand, is more about analyzing and predicting. It involves algorithms like decision trees, linear regression, logistic regression, and k-means clustering that learn from data to make predictions or classify things. You feed it data, and it tells you something about it—like whether an email is spam, or what your next sales numbers might be.
To put it simply:
Generative AI = “Make something new from what you’ve learned.”
Traditional ML = “Understand patterns and make decisions based on them.”
#genai
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