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 predictions. It creates a f

Bayesian networks Algorithm Concepts of Bayesian networks A Deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)  Each node is associated with a probability distribution, and the edges represent the conditional dependencies between those distributions.   Bayesian networks can be used for probabilistic reasoning and decision-making by inferring the probability of a particular event, given some evidence or observations. They can also be used for decision-making by selecting the action that maximizes some expected utility.   One of the most popular algorithms for probabilistic reasoning in Bayesian networks is called the belief propagation algorithm . This algorithm uses a message-passing approach to compute the marginal probabilities of the nodes in the network.   Let's consider an example to illustrate how Bayesian networks can be used for probabilistic reasoning and decision-making. Suppose we want to predict whether a

Machine Learning Association Rule Mining Algorithms  Concepts of Association Rule Mining Association Rule Mining is a technique of data mining used for finding co-occurrence relationships and patterns in large datasets. It is employed to glean intriguing connections between variables in sizable databases. The relationships discovered in Association Rule Mining are represented in the form of rules, where the antecedent and consequent are a set of items.   There are several algorithms used in Association Rule Mining, such as Apriori, FP-Growth, ECLAT, and more. Among these algorithms, Apriori is the most widely used algorithm for Association Rule Mining.   The item sets that do not match the minimal support criterion are pruned by the Apriori algorithm after it generates a candidate set of item sets. The support threshold is a user-defined value that determines the minimum frequency of an item set to be considered as frequent. Association Rule Mining  Algorithm Define the problem and

Machine Learning  Decision Boundary Algorithms Decision Boundary Algorithms Concepts Decision boundary algorithms are used in machine learning to create a boundary that separates different classes or groups in a dataset. They are used to classify data points based on their features or attributes. Some popular decision boundary algorithms include decision trees, random forests, logistic regression, and support vector machines.   One example of a decision boundary algorithm is the logistic regression algorithm. To determine the likelihood of a binary outcome (such as "yes" or "no"), a binary classification procedure known as logistic regression is utilized(yes/no, true/false). It creates a decision boundary by fitting a logistic function to the training data.   Let's consider the example of a dataset containing information about a bank's customers, including their age and credit score, as well as whether they have defaulted on a loan. We can use logistic r

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

Convolutional Neural Networks and Recurrent  Neural Networks Algorithms Convolutional NN - Recurrent NN Concepts Neural networks, including deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) The machine learning technique known as neural networks was inspired by the design and function of the human brain. They consist of interconnected nodes, or "neurons", that process and transmit information to each other to make a prediction or decision. Deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are advanced neural network structures that have proven to be highly effective in a variety of applications including time series analysis, natural language processing, and picture and audio recognition. Convolutional Neural Networks (CNNs) are a type of neural network that is designed to process and analyze image data. They use convolutional layers, which apply