AI vs ML vs Neural Network vs Deep Learning

๐€๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐ข๐š๐ฅ ๐ˆ๐ง๐ญ๐ž๐ฅ๐ฅ๐ข๐ ๐ž๐ง๐œ๐ž (๐€๐ˆ):

- AI refers to the simulation of human intelligence processes by machines, especially computer systems.

- It encompasses a broad range of techniques and approaches aimed at enabling machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from experience.

๐Œ๐š๐œ๐ก๐ข๐ง๐ž ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐  (๐Œ๐‹):

- Machine learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed.

- It involves algorithms that allow computers to learn from and make predictions or decisions based on data.

- Machine learning techniques include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

๐๐ž๐ฎ๐ซ๐š๐ฅ ๐๐ž๐ญ๐ฐ๐จ๐ซ๐ค๐ฌ:

- Neural networks are a class of machine learning algorithms inspired by the structure and function of the human brain.

- They consist of interconnected nodes, or โ€œneurons,โ€ organized in layers. Information flows through the network, with each layer processing and transforming the input data to produce an output.

- Neural networks are capable of learning complex patterns and relationships in data, making them powerful tools for tasks like image and speech recognition, natural language processing, and more.

๐ƒ๐ž๐ž๐ฉ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐ :

- Deep learning is a subfield of machine learning and neural networks that focuses on learning representations of data through the use of deep architectures, typically with many layers of neurons.

- It leverages large amounts of labeled data and computational power to automatically learn hierarchical representations of data.

- Deep learning has achieved remarkable success in various domains, including computer vision, speech recognition, natural language processing, and autonomous vehicles, among others.

In summary, AI is the overarching field focused on creating intelligent systems, machine learning is a subset of AI that emphasizes learning from data, neural networks are a class of algorithms used in machine learning inspired by the brain, and deep learning is a subfield of machine learning and neural networks that involves learning from large amounts of data using deep architectures.

FAQ's

Objectives:

  • Install and configure Python and the necessary AI libraries, such as NumPy, Pandas, Matplotlib, and Scikit-Learn.
  • Learn the basics of Linux command-line tools for AI, such as SSH, Git, and Docker.
  • Perform exploratory data analysis on datasets using Python, including data cleaning, visualization, and feature engineering.
  • Understand the basics of statistical concepts relevant to AI, such as probability, distributions, and hypothesis testing.
  • Implement basic AI algorithms in Python, such as linear regression, logistic regression, and decision trees.

Learning Outcomes:

  • Students will be able to set up a Python environment for AI development.
  • Students will be able to use Linux command-line tools for AI.
  • Students will be able to perform exploratory data analysis using Python.
  • Students will be able to explain statistical concepts relevant to AI.
  • Students will be able to implement basic AI algorithms in Python.

Objectives:

  • Learn the basics of supervised learning algorithms such as support vector machines, gradient boosting machines, and random forests.
  • Understand ensemble techniques such as bagging, boosting, and stacking.
  • Implement supervised learning and ensemble algorithms in Python.

Learning Outcomes:

  • Students will be able to explain the basics of supervised learning algorithms.
  • Students will be able to understand ensemble techniques.
  • Students will be able to implement supervised learning and ensemble algorithms in Python.

Objectives:

  • Learn the different techniques for tuning machine learning models, such as cross-validation and hyperparameter optimization.
  • Understand the basics of unsupervised learning algorithms such as k-means clustering and hierarchical clustering.
  • Implement model tuning and unsupervised learning algorithms in Python.

Learning Outcomes:

  • Students will be able to explain the different techniques for tuning machine learning models.
  • Students will be able to understand the basics of unsupervised learning algorithms.
  • Students will be able to implement model tuning and unsupervised learning algorithms in Python.

Objectives:

  • Learn the basics of neural networks and deep learning.
  • Implement basic deep learning models such as perceptrons, convolutional neural networks, and recurrent neural networks using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of neural networks and deep learning.
  • Students will be able to implement basic deep learning models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of computer vision concepts such as image processing, object detection, and image classification.
  • Implement basic computer vision models using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of computer vision concepts.
  • Students will be able to implement basic computer vision models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of recommendation systems and natural language processing.
  • Implement basic recommendation systems and natural language processing models using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of recommendation systems and natural language processing.
  • Students will be able to implement basic recommendation systems and natural language processing models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of generative AI and large language models.
  • Deploy a ChatGPT-like app to the cloud.

Learning Outcomes:

  • Students will be able to explain the basics of generative AI and large language models.
  • Students will be able to deploy a ChatGPT-like app to the cloud.

Objectives:

  • Learn the basics of reinforcement learning.
  • Deploy a simple AI model to the cloud using a cloud platform such as AWS, Azure, or Google Cloud Platform.

Learning Outcomes:

  • Students will be able to explain the basics of reinforcement learning.
  • Students will be able to deploy a simple AI model to the cloud using a cloud platform such.

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