Types of AI

🤖 AI/ML Roadmap

1️⃣ Math & Stats 🧮🔢: Learn Linear Algebra, Probability, and Calculus.
2️⃣ Programming 🐍💻: Master Python, NumPy, Pandas, and Matplotlib.
3️⃣ Machine Learning 📈🤖: Study Supervised & Unsupervised Learning, and Model Evaluation.
4️⃣ Deep Learning 🔥🧠: Understand Neural Networks, CNNs, RNNs, and Transformers.
5️⃣ Specializations 🎓🔬: Choose from NLP, Computer Vision, or Reinforcement Learning.
6️⃣ Big Data & Cloud ☁️📡: Work with SQL, NoSQL, AWS, and GCP.
7️⃣ MLOps & Deployment 🚀🛠️: Learn Flask, Docker, and Kubernetes.
8️⃣ Ethics & Safety ⚖️🛡️: Understand Bias, Fairness, and Explainability.
9️⃣ Research & Practice 📜🔍: Read Papers and Build Projects.
🔟 Projects 📂🚀: Compete in Kaggle and contribute to Open-Source.

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AI vs ML vs Deep Learning 🤖

You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not.

AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning.

ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed.

Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc.

Think of it this way:
AI = Science
ML = A chapter in the science
Deep Learning = A paragraph in that chapter.

Want to become a Data Scientist?

Here’s a quick roadmap with essential concepts:

1. Mathematics & Statistics

Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.

Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.

Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.


2. Programming

Python or R: Choose a primary programming language for data science.

Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.

R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.


SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.


3. Data Wrangling & Preprocessing

Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.


4. Data Visualization

Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.


5. Machine Learning

Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.


6. Advanced Machine Learning & Deep Learning

Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.


7. Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.


8. Big Data Tools (Optional)

Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.


9. Data Science Workflows & Pipelines (Optional)

ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).


10. Model Validation & Tuning

Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.


11. Time Series Analysis

Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.


12. Experimentation & A/B Testing

Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.

ENJOY LEARNING 👍👍

#datascience

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Instruction-tuned 4B ⚡ Optimized for fast, accurate responses (non-thinking mode). Excels in logic, coding & creative tasks.

🖼️ google/gemma-3-4b-it
Multimodal 4B 🖊️ Handles text + images with 128K context. Great for QA, summarization & fine-tuning.

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Agentic reasoning model 🔍 Built for the Jan app. Tool use + strong reasoning, 91% accuracy on SimpleQA.

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