Machine learning and Artificial Intelligence (AI) are often used interchangeably, but they are not the same thing. AI refers to the capability of machines to perform tasks that would typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Machine learning, on the other hand, is a subset of AI that focuses on the development of algorithms that enable machines to learn from data, identify patterns, and make predictions.

 

One key difference between machine learning and AI is that AI is a broader concept that encompasses several areas of computer science, including natural language processing, computer vision, and robotics. Machine learning, in comparison, is a more specific area of AI that focuses on creating algorithms that enable machines to learn and improve on their own without being explicitly programmed.

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Another key difference between AI and machine learning is the extent of human involvement in the process. AI requires a human to develop and program the system, while machine learning algorithms are capable of learning and improving on their own without explicit human intervention.

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In the business world, it is important to understand the benefits of machine learning and AI. Machine learning algorithms can be used to automate processes, analyze large amounts of data, and make predictions, which can lead to increased efficiency, improved decision-making, and enhanced customer experiences. For example, machine learning algorithms can be used to analyze customer data to identify trends and preferences, which can inform personalized marketing campaigns and improve customer satisfaction.

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In conclusion, while AI and machine learning are closely related, they are not the same thing. AI refers to the capability of machines to perform tasks that would typically require human intelligence, while machine learning is a subset of AI that focuses on the development of algorithms that enable machines to learn from data. Understanding the benefits of machine learning and AI can help businesses make data-driven decisions and improve processes to remain competitive in today's market.

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Things to keep in mind in Deep learning: 

 

Classifications - Categorizing a group of objects while using some basic data features that describe them. Activation of a classifier produces scores that can be used to measure confidence score. 

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Neural Networks - Neural networks are highly structured layers used when the output can fall into one of two categories.  Neural networks will have many layers depending on the complexity of the patterns you are introducing. Category of layers include the Input layer, the hidden layers and the output layer. Each hidden layer can have its own classifiers which passes it's results to the next layer until it reaches the output layer where the result is based on data that has gone through each node of hidden layer producing a high confidence score. This process from the input layer to the output layer to classifying a set of inputs is called Forward Propagation. 

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Here is a good visual on how a Neural Network works. 

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