Google opens it's first artificial intelligence (AI) Research center in Africa
Location; Accra Ghana π¬π
Location; Accra Ghana π¬π
Sure they'll be opening more soon in other countries in Africa
React to this post if you're online π
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π©βπ» Learn PROGRAMMING π¨βπ»
Which Subfield Should I Begin With ? π€
Great!
Machine learning will be the first subfield.
It will be very long and simple to understand, so it will be posted in sections over the next few days to ensure that everyone understands it.
Machine learning will be the first subfield.
It will be very long and simple to understand, so it will be posted in sections over the next few days to ensure that everyone understands it.
Machine Learning Tutorial for Beginners: What is, Basics of ML
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What is Machine Learning?
Machine Learning is a system of computer algorithms that can learn from example through self-improvement without being explicitly coded by a programmer. Machine learning is a part of artificial Intelligence which combines data with statistical tools to predict an output which can be used to make actionable insights.
The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input and uses an algorithm to formulate answers.
A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the userβs historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.
Machine learning is also used for a variety of tasks like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.
Machine Learning is a system of computer algorithms that can learn from example through self-improvement without being explicitly coded by a programmer. Machine learning is a part of artificial Intelligence which combines data with statistical tools to predict an output which can be used to make actionable insights.
The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input and uses an algorithm to formulate answers.
A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the userβs historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.
Machine learning is also used for a variety of tasks like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.
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Machine Learning vs. Traditional Programming
Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed.
Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.
Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed.
Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.
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βTraditional Programming
Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.
Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.
We will continue with How machine learning works tomorrow
About this item
π€Now, battle in Style with Shadow's razor-sharp Edgy design and 7-colour Rainbow lighting with breathing effect.
π€It has a gaming sensor for precise and fast-tracking with 4-levels of DPI adjustments(1200, 1800, 2400 & 3600) that won't let you down in the hour of your need.
π€Made of Gaming Grade materials with 6-buttons tested for more than 7-million clicks, so we are here for a long term commitment, are you? Note: The side buttons on the mouse are non programmable and can be used as Forward and backward buttons.
π€Comes with a 1.8 Metre long and durable braided cable with an EMI suppressor to cancel out noise in Data Transmission.
https://amzn.to/3nkiQ6f
π€Now, battle in Style with Shadow's razor-sharp Edgy design and 7-colour Rainbow lighting with breathing effect.
π€It has a gaming sensor for precise and fast-tracking with 4-levels of DPI adjustments(1200, 1800, 2400 & 3600) that won't let you down in the hour of your need.
π€Made of Gaming Grade materials with 6-buttons tested for more than 7-million clicks, so we are here for a long term commitment, are you? Note: The side buttons on the mouse are non programmable and can be used as Forward and backward buttons.
π€Comes with a 1.8 Metre long and durable braided cable with an EMI suppressor to cancel out noise in Data Transmission.
https://amzn.to/3nkiQ6f
π1
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βHow does Machine Learning Work?
Now in this Machine learning basics for beginners tutorial, we will learn how Machine Learning (ML) works:
Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if its feed a previously unseen example, the machine has difficulties to predict.
The core objective of machine learning is the learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.
The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.
Now in this Machine learning basics for beginners tutorial, we will learn how Machine Learning (ML) works:
Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if its feed a previously unseen example, the machine has difficulties to predict.
The core objective of machine learning is the learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.
The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.
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