• Python: The most widely used language for ML and AI, thanks to its simplicity and powerful libraries (TensorFlow, Scikit-learn, Pandas). It allows you to build and train machine learning models efficiently.
• SQL: Helps in managing, retrieving, and querying data from databases, a crucial skill for working with real-world datasets.
• Linux: A preferred operating system for running AI models on servers and cloud platforms. Knowing basic commands will help you manage environments and automate tasks.

• Probability & Statistics: Essential for understanding data distributions, predictions, and making decisions under uncertainty (e.g., probability distributions, hypothesis testing).
• Linear Algebra: The backbone of machine learning models, especially for neural networks and transformations like PCA. Concepts like matrices and vectors are fundamental.
• Calculus: Used to optimize models through algorithms like gradient descent, which is key to training neural networks.

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• Supervised Learning: Algorithms like linear regression, decision trees, and support vector machines (SVM) that use labeled data to make predictions.
• Unsupervised Learning: Methods like K-means clustering and Principal Component Analysis (PCA) to discover patterns in unlabeled data. Useful for data grouping and dimensionality reduction.
• Deep Learning: Advanced models that mimic the human brain, such as neural networks (CNNs for image processing and RNNs for sequential data like text).

Before feeding data into models, it’s critical to clean and prepare it. This involves:

• Handling missing data, removing outliers.
• Feature scaling (normalizing data), encoding categorical variables, and feature engineering.

• Cross-validation: Splitting data into train/test sets or using K-fold validation to avoid overfitting and improve model performance.
• Evaluation metrics: Metrics like accuracy, precision, recall, F1 score, and ROC-AUC help assess model performance.

Focuses on training agents to make sequential decisions by interacting with an environment. The agent learns by trial and error to maximize cumulative rewards (e.g., building bots that play games like Pac-Man).

• • A branch of AI that deals with understanding and processing human language. This includes tasks like tokenization (breaking text into words), sentiment analysis, and text classification (e.g., spam detection).

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Tools like Matplotlib and Seaborn help visualize complex datasets, enabling better analysis through graphs, plots, and charts. It’s crucial for identifying trends and patterns in data.

Focuses on models that generate new data, like Large Language Models (LLMs) (e.g., OpenAI’s GPT) and Generative Adversarial Networks (GANs), which can generate images, texts, and more. These are used in content creation, chatbots, and simulations.