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Introduction to AI & ML

Artificial Intelligence (AI) and Machine Learning (ML) are two closely related fields that deal with the development of computer systems capable of performing tasks that typically require human intelligence. While AI is a broader concept that encompasses various aspects of intelligent systems, ML is a subset of AI that focuses on algorithms and models that allow computers to learn from data and make predictions or judgements based on it.

AI, or Artificial Intelligence, is a subfield of computer science concerned with creating intelligent machines capable of performing tasks that would ordinarily need human intelligence. AI is creating algorithms and models that allow robots to comprehend, learn from, and respond to their surroundings or data.

ML, or Machine Learning, is a subfield of AI that focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. ML algorithms learn from data and iteratively improve their performance through experience.

In summary, AI aims to create intelligent machines that can mimic human intelligence, while ML is a specific approach within AI that focuses on developing algorithms that can learn from data and improve their performance over time.

Why should you take AI & ML?

AI & ML (Artificial Intelligence and Machine Learning) should be pursued because they offer immense potential to revolutionize various industries and fields. They enable automation, data analysis, pattern recognition, and prediction capabilities that can drive innovation, efficiency, and decision-making. By understanding and applying AI & ML, individuals and organizations can leverage the power of data to gain insights, optimize processes, and create intelligent systems that improve productivity, solve complex problems, and enhance user experiences.

Frequently Asked Questions

Artificial intelligence (AI) is a method for creating systems that replicate human behaviour or decision-making.

Machine Learning is a subset of artificial intelligence that uses data to solve problems. These solvers are data-trained models that learn from the information presented to them. This data comes from probability theory and linear algebra. ML systems use our data to learn and solve prediction tasks automatically.

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To learn Machine Learning (ML), some prerequisites include a basic understanding of mathematics (linear algebra, calculus, probability), programming skills (Python is commonly used), familiarity with data manipulation and analysis, and knowledge of statistics. A strong foundation in these areas will help grasp the concepts and techniques involved in ML effectively.

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Freshers - AI & ML Interview Questions and Answers

Artificial Intelligence (AI) is a broader field that focuses on creating intelligent machines capable of simulating human intelligence. Machine Learning (ML) is a subset of AI that specifically deals with algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

There are several types of Machine Learning algorithms, including:

  • Supervised Learning: Algorithms that learn from labelled training data to make predictions or classify new instances.
  • Unsupervised Learning: Algorithms that identify patterns and structures in unlabeled data, without specific outcome labels.
  • Reinforcement Learning: Algorithms that learn through trial and error interactions with an environment, maximizing rewards.
  • Deep Learning: Neural network-based algorithms that can learn complex representations from large datasets.
  • Decision Trees: Algorithms that use a tree-like model to make decisions or predictions based on input features
  • Overfitting occurs when a Machine Learning model learns the training data too well, to the point where it performs poorly on unseen or test data. It happens when the model becomes too complex or captures noise and irrelevant patterns from the training data, failing to generalize well to new instances.

    Classification is a type of Machine Learning task where the goal is to predict discrete class labels or categories for given input data. Regression, on the other hand, is a task where the goal is to predict continuous numerical values or quantities based on input variables.

    Handling missing or null values in a dataset can be done in a few ways:

  • Removing instances: If the missing values are few and the dataset is large, removing instances with missing values may not significantly affect the overall analysis.
  • Imputation: Fill in the missing values with estimated or calculated values based on the available data (e.g., mean, median, or regression imputation).
  • Using algorithms: Some Machine Learning algorithms can handle missing values inherently, allowing the model to learn patterns even with missing data.