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🛣️ AI Fundamentals Roadmap

1. Understanding Artificial Intelligence

  • What is AI? Defining AI, its different types (narrow, general, super), and core concepts.
  • Problem Types AI Solves: Classification, regression, clustering, recommendation, etc.
  • History of AI: Key milestones and breakthroughs.
  • AI Hardware Requirements: Processors for AI, AI as a Service (AIaaS)

2. Mathematics for AI

  • Linear Algebra: Vectors, matrices, linear transformations. Essential for manipulating data and understanding model weights.
  • Calculus: Derivatives, gradients, optimization (crucial for training ML models).
  • Probability and Statistics: Distributions, statistical concepts (mean, variance, Bayes' Theorem) for data analysis and model uncertainty.

3. Machine Learning

  • Supervised Learning: Algorithms learning from labeled data (e.g., classification, regression).
    • Linear Regression: Often a starting point. Learn how to model the relationship between a dependent variable and independent variables. This forms the basis for understanding more complex models.
    • Logistic Regression: Despite the name, it's for classification problems (predicting categories: spam vs. not spam).
    • Decision Trees: Understand how a tree-like structure with branching decisions can be used for classification or regression.
    • Support Vector Machines (SVMs)
  • Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering, dimensionality reduction).
    • K-means Clustering
    • Principal Component Analysis (PCA)
  • Reinforcement Learning: An agent learns by interacting with an environment and receiving rewards or penalties (like training a game-playing AI).
  • Overfitting and Underfitting: Crucial concepts to diagnose if your model is too specific or too general. Know the signs and how to address them.
  • Model Evaluation: Metrics like accuracy, precision, recall, F1-score, ROC curves for performance assessment.

4. Deep Learning

  • Neural Networks: The foundation of deep learning, inspired by biological neurons.
  • Key Architectures:
    • Convolutional Neural Networks (CNNs): Superb for image processing.
    • Recurrent Neural Networks (RNNs): Ideal for sequential data (text, time series).
  • Backpropagation: Algorithm for training neural networks.
  • Libraries:
    • TensorFlow
    • PyTorch

5. Natural Language Processing (NLP)

  • Text Processing: Tokenization, stemming, lemmatization.
  • Representing Text: Word embeddings (Word2Vec, GloVe).
  • Tasks: Sentiment analysis, text classification, machine translation.

6. Computer Vision (CV)

  • Image Processing: Filtering, resizing, transformations.
  • Object Detection: Identifying objects and their locations within images.
  • Image Classification: Assigning labels to images.

7. Important Tools & Techniques

  1. Data Preprocessing and Cleaning: Real-world data is messy. Learn techniques to handle missing data, outliers, and formatting issues.
  2. Feature Engineering: The art of transforming and creating features that improve your model's performance.
  3. Scikit-learn: Mastering this Python library will give you access to a vast array of ML algorithms and tools.
  4. Gradient Descent: An optimization algorithm common for training many ML models, understanding the basics gives insights into how models learn.

Learning Resources​

AI Fundamentals

Python for Machine Learning

Neural Networks and Deep Learning

Natural Language Processing

Open Source AI

Specializations

AI Communities

  • Kaggle Kaggle is the world’s largest data science and machine learning community. More than ten million registered users visit Kaggle to learn, find data, compete, and collaborate on the cutting edge of machine learning.
  • HuggingFace: An AI community focused on Open Source Models, Datasets & Projects.