What is AI and Machine learning
Today machine learning has a wide range of application and most of them are technologies that we encounter daily. For instance ,Netflix or similar OTT platforms use machine learning to personalize suggestion for each user . So if the user frequently watches crime thrillers or searches for the same ,the platform’s ML-powered recommendation system will start suggesting more movies of a similar genre . Likewise, Facebook and Instagram personalize a user’s feed based on posts they frequently interact with.
The term Machine learning was used by Arthur Samuel in 1959 in a trail based testing in computer gaming and artificial intelligence.
Machine learning is a subset of artificial intelligence. It is based on the concept that software programs can learn from data, specific patterns, and make decision with minimal human interference. In other words ,ML is an area of computational science that enables a user to feed an enormous amount of data to an algorithm and have the system analyze and make a data driven decisions based on input data. Therefore, ML algorithms do not reply on a predetermined model and instead directly learn information from the fed data .
Is Domain knowledge important for machine learning
Domain knowledge helps you understand how data is collected and therefore proper preprocessing methods. With domain knowledge, you can get hints on features that may be useful to your model.
Types of Machine Learning
Machine learning classification is based on how an algorithm learns to become more accurate at predicting outcomes. Thus there are three basic approaches to machine learning which are supervised learning, unsupervised learning, and reinforcement learning .
In supervised machine learning, the algorithms are supplied with labelled training data. Plus, the user defines the variables they want the algorithms to assess, the target variables are the variables that help us predict the target. So its more like we show the algorithms a fish’s image and says It’s a fish . Then when we show frog and point it out to be a frog. Then, when the algorithms has been trained on enough fish and frog data, it will learn to differentiate between them .
Unsupervised machine learning involves algorithms that learn from unlabelled training data. So ,there are only the features and no target variables, Unsupervised learning problems include clustering, where input variable with the same characteristics are grouped and associated to decipher meaningful relationships within the data set. An example of clustering is grouping people into smoker and non-smokers. On the contrary, discovering that customers using smartphones will also buy phone cover is association.
Reinforcement learning is a feed-based technique is which the machine learning models learn to make a series of decisions based on the feedback they receive for their actions. For each good action, the machine get positive feedback, and for each bad one, its gets a penalty or a negative feedback. So unlike supervised machine learning , a reinforcement model automatically learns using feedback instead of any labelled data.
Why use Python for machine learning?
Machine learning projects differ from traditional software projects in that the former involves distinct skill sets, technology stack, and deep research. Therefore, implementing a successful machine learning project requires a programming language that stable, flexible and offers robust tools. Python offers it all , so we mostly see Python-based machine learning projects .
Python’s fashionability is largely due to the fact that it's a platform-independent language and is supported by utmost platforms, including Windows, macOS, and Linux. therefore, inventors can produce standalone executable programs on one platform and distribute them to other operating systems without taking a Python practitioner. thus, training machine literacy models come more manageable and cheaper.
Simplicity and flexibility
Behind every machine learning model are complex algorithms and workflows that can intimidate and overwhelm the user. However, Python's concise, easy-to-read code allows developers to focus on machine learning models without worrying about the technical details of the language. In addition, Python is easy to learn and can handle complex machine learning tasks, enabling rapid prototyping and testing.
Wide selection of Framework and libraries
Python offers a wide selection of frameworks and libraries that can significantly reduce development time. Such libraries have pre-built code that developers use to perform common programming tasks. Python's software tool repertoire includes Scikit-learn, TensorFlow and Keras for machine learning, Pandas for general data analysis, NumPy and SciPy for data analysis and scientific computing, and Seaborn for data visualization
What is a Deep Learning Network?
Deep Learning Network or Deep Neural Network (DNN) is a branch of machine learning based on mimicking the human brain. A DNN contains an entity that combines multiple inputs to produce a single output. They are similar to biological neurons that receive multiple signals through synapses and send a single stream of action potentials through the neurons.
In a neural network, brain-like functions are achieved by a node layer consisting of an input layer, one or more hidden layers, and an output layer. Each artificial neuron or node has an associated threshold and weight and is connected to another artificial neuron. When the node's output exceeds the defined threshold, the node wakes up and sends data to the next layer in the network.
DNN relies on training data to train and optimize accuracy over time. These are robust artificial intelligence tools that can perform fast data classification and clustering. Two of the most common application domains for deep neural networks are image recognition and speech recognition.
Whether you're using Face ID to unlock your phone, watch a movie, or search for random topics on Google, modern digital consumers will find superficial recommendations and more. We are looking for excellent personalization. Regardless of industry or domain, AI continues to play a key role in improving the user experience. In addition, Python's simplicity and versatility make it convenient and efficient to develop, deploy, and maintain AI projects across platforms.
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