PPT On Machine Learning

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This ppt contains Introduction to Machine Learning from basics, everything covered in simple way to understand...any doubts free to ask

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Introduction to Machine Learning: A subset of artificial intelligence known as machine learning focuses primarily on the creation of algorithms that enable a computer to independently learn from data and previous experiences. Arthur Samuel first used the term "machine learning" in 1959. It could be summarized as follows: Without being explicitly programmed, machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things. A machine can learn if it can gain more data to improve its performance.
How does Machine Learning work: • A machine learning system builds prediction models, learns from previous data, and predicts the output of new data whenever it receives it. • The amount of data helps to build a better model that accurately predicts the output, which in turn affects the accuracy of the predicted output. UNSUPERVISED LEARNING CLUSTERING data MACHINE LEARNING CLASSIFICATION SUPERVISED LEARNING both REGRESSION
Features of Machine Learning. Machine learning uses data to detect various patterns in a given dataset. It can learn from past data and improve automatically. It is a data-driven technology. Machine learning is much similar to data mining as it also deals with the huge amount of the data. Need for Machine Learning: The demand for machine learning is steadily rising. Because it is able to perform tasks that are too complex for a person to directly implement, machine learning is required. Following are some key points which show the importance of Machine Learning: Rapid increment in the production of data • Solving complex problems, which are difficult for a human Decision making in various sector including finance Finding hidden patterns and extracting useful information from data.
Classification of Machine Learning At a broad level, machine learning can be classified into three types: 1.Supervised learning 2.Unsupervised learning 3.Reinforcement learning Supervised Learning Classification Reinforcement of Learning Machine Learning Unsupervised Learning
comparison Definition Tvpe Of data Type Of problems Supe n Algorithms Application Supervised ML Learns by using labelled data Label led data Regression and classification Extra supervision Linear Regression, svM, KNN etc. Logistic Regression, Calculate outcomes Risk Evaluation, Forecast Sales unsupervised ML Trained using unlabelled data Without any guidance. unlabelled data Association and Clustering NO supervision K — Means, C — Means, Apriori Discover underlying patterns Recommendation Svstem, Anomaly Det n Reinforcement ML Works on interacting With the environment NO — predefined data Exploitation or Exploration NO supervision Q — Learning, Learn a series Of action Self Driving Cars, Gaming, Healthcare
• supervised learning: • In supervised learning, the machine is trained on a set of labeled data, which means that the input data is paired with the desired output. The machine then learns to predict the output for new input data. Supervised learning is often used for tasks such as classification, regression, and object detection. • For example, a labeled dataset of images of Elephant, Camel and Cow would have each image tagged with either "Elephant" , "Camel"or "Cow." Supervised Learning
• Key Points: • Supervised learning involves training a machine from labeled data. • Labeled data consists of examples with the correct answer or classification. • The machine learns the relationship between inputs (fruit images) and outputs (fruit labels). • The trained machine can then make predictions on new, unlabeled data. • Supervised learning can be further divided into two types: 1.Classification 2. Regression 1. Classification - Supervised Learning • Classification is used when the output variable is categorical i.e. with 2 or more classes. For example, yes or no, male or female, true or false, etc.
the In order to predict whether a mail is spam or not, we need to first teach the machine what a spam mail is. This is done based on a lot of spam filters - reviewing the content of the mail, reviewing the mail header, and then searching if it contains any false information. Certain keywords and blacklist filters that blackmails are used from already blacklisted spammers. Based on the content, label, and the spam score of the new incoming mail, the algorithm decides
2. Regression - Supervised Learning Regression is used when the output variable is a real or continuous value. In this case, there is a relationship between two or more variables i.e., a change in one variable is associated with a change in the other variable. For example, salary based on work experience or weight based on height, etc. Temperature Let's consider two variables - humidity and temperature. Here, 'temperature' is the independent variable and 'humidity' is the dependent variable. If the temperature increases, then the humidity decreases. These two variables are fed to the model and the machine learns the relationship between them. After the machine is trained, it can easily predict the humidity based on the given temperature.
What is Unsupervised learning? • Unsupervised learning is a type of machine learning that learns from unlabeled data. This means that the data does not have any pre-existing labels or categories. The goal of unsupervised learning is to discover patterns and relationships in the data without any explicit guidance. Unsupervised Learning
Key Points: • Unsupervised learning allows the model to discover patterns and relationships in unlabeled data. Clustering algorithms group similar data points together based on their inherent characteristics. • Feature extraction captures essential information from the data, enabling the model to make meaningful distinctions. Label association assigns categories to the clusters based on the extracted patterns and characteristics.
Unsupervised learning can be further grouped into types: I .Clustering 2.Association l. Clustering - Unsupervised Learning Clustering is the method of dividing the objects into clusters that are similar between them and are dissimilar to the objects belonging to another cluster. For example, finding out which customers made similar product purchases.
• you can see a graph where customers are grouped. Group A customers use more data and also have high call durations. Group B customers are heavy Internet users, • while Group C customers have high call duration. • So, Group B will be given more data benefit plants, Group C will be given cheaper called call rate plans group A wi112. Association - Unsupervised Learning 2.Association Association is a rule-based machine learning to discover the probability of the co- occurrence of items in a collection. • For example, finding out which products were purchased together. be given the benefit of both.
Milk Wheat Bread Milk Rice Cu stomer3 If new bread. is likely to too Let's say that a customer goes to a supermarket and buys bread, milk, fruits, and wheat. Another customer comes and buys bread, milk, rice, and butter. Now, when another customer comes, it is highly likely that if he buys bread, he will buy milk too. Hence, a relationship is established based on customer behavior and recommendations are made.
3.Reinforcement learning : Reinforcement Learning is a part of machine learning. Here, agents are self-trained on reward and punishment mechanisms. It's about taking the best possible action or path to gain maximum rewards and minimum punishment through observations in a specific situation. It acts as a signal to positive and negative behaviors. Reinforcernent Learning in ML Age t
Su pe rvised Lea r n i n g Training Info : Output Outputs Error Reinforcernent Learning "I-raining Evaluations Inputs Rei n Outputs: 'Wtions Objective: Get as much reward as possible
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PPT On Machine Learning

Pragnya / Hyderabad

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