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Learn Machine Learning Algorithms

Machine Learning Algorithms with Python Code Contents of Algorithms  1.  ML Linear regression A statistical analysis technique known as "linear regression" is used to simulate the relationship between a dependent variable and one or more independent variables. 2.  ML Logistic regression  Logistic regression: A statistical method used to analyse a dataset in which there are one or more independent variables that determine an outcome. It is used to model the probability of a certain outcome, typically binary (yes/no). 3.  ML Decision trees Decision trees: A machine learning technique that uses a tree-like model of decisions and their possible consequences. It is used for classification and regression analysis, where the goal is to predict the value of a dependent variable based on the values of several independent variables. 4.  ML Random forests Random forests: A machine learning technique that uses multiple decision trees to improve the accuracy of predicti...
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What is Naive Bayes algorithm

Naive Bayes Algorithm with Python Concepts of Naive Bayes Naive Bayes is a classification algorithm based on Bayes' theorem, which states that the probability of a hypothesis is updated by considering new evidence. Since it presumes that all features are independent of one another, which may not always be the case in real-world datasets, it is known as a "naive". Despite this limitation, Naive Bayes is widely used in text classification, spam filtering, and sentiment analysis. Naive Bayes Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., Naive Bayes). Compute the prior probability and likelihood of each class based on the training data. Use Bayes' theorem to compute the posterior probability of each class given the input features. Classify the input by choosing the class with the highest posterior probability. Evaluate the model on a test dataset to estimate its performance. Here's an example code in Python for Naive Bayes: Python cod...
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What is Reinforcement Learning Algorithm

Machine Learning  Reinforcement Learning Algorithms Reinforcement Learning Concepts Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by taking actions and receiving rewards or punishments. Learning a policy that maximizes the cumulative reward across a series of actions is the aim of reinforcement learning. Two common reinforcement learning algorithms are Q-learning and Deep Q-Networks (DQNs). Q-learning r einforcement learning algorithm Q-learning is a model-free, off-policy reinforcement learning algorithm. In Q-learning, the agent learns an action-value function, called a Q-function, which estimates the expected cumulative reward for taking a particular action in a particular state. The Q-function can be represented as a lookup table or a neural network. The Q-function is updated using the Bellman equation: Q(s,a) = Q(s,a) + α(r + γmax(Q(s',a')) - Q(s,a)) where Q(s, a) is the Q-value for taking action an in stat...
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What is Support Vector Machines

Support Vector Machine Learning Algorithm Concepts of Support Vector Machines The supervised learning technique Support Vector Machines (SVM) can be applied to both classification and regression applications. Finding the ideal hyperplane that categorizes the data points is how it operates. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. Here is an example of how SVM works : Suppose we have a dataset of students categorized into two classes based on their grades in two subjects, say Physics and Math. We want to predict whether a new student will pass or fail based on their grades in Physics and Math. We use SVM to identify the optimal hyperplane that separates the passing and failing students. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. The new student is then assigned to the class on the other side of the hyperplane. Support Vector Machin...
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What is Random Forests

Random Forests Algorithm Concepts of Random forests Random forests are an ensemble learning method that combines multiple decision trees to create a more accurate and robust model. In a random forest, multiple decision trees are trained on random subsets of the data and features, and the final prediction is made by averaging the predictions of the individual trees. For example, let's say we have a dataset of customer information, including age, income, education level, and purchase history. We can use a random forest to predict whether a customer will make a purchase based on these attributes. Random forests  Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., random forests). Split the data into training and validation sets. Construct multiple decision trees using random subsets of the data and features. Aggregate the predictions from all the trees to make a final prediction. Evaluate the model on the validation set to estimate its performance. Apply th...
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