ML Machine Learning Introduction to Machine Learning Industries across healthcare nance and technology are seeking professionals who can apply machine learning to solve real world problems and make data driven decisions This has led to a high demand for machine learning skills in the job market In this Edureka machine learning full course video you will gain a thorough understanding of machine learning from theory to practical applications If you want to learn more about machine learning after watching this session and obtain Edureka s machine learning certi cation course see the link in the description below Introduction to machine learning Becoming a machine learning engineer Machine learning algorithms linear regression logistic regression decision tree algorithm random forest and k nearest neighbor algorithm Technical aspects of machine learning naive Bayes classi er support vector machine and k means clustering algorithm Why mathematics is required for machine learning Model deployment in machine learning and machine learning on cloud Agenda 1 A career in machine learning skills jobs resumes salaries and tools and frameworks required Important machine learning interview questions with answers What is Machine Learning Machine learning is a sub eld of arti cial intelligence that focuses on the design of systems that can learn from and make decisions and predictions based on experience which is data in the case of machines Machine learning enables computers to act and make data driven decisions rather than being explicitly programmed to carry out a certain task These programs are designed to learn and improve over time when exposed to new data AI Machine Learning and Deep Learning Arti cial intelligence is a broader concept of machines being able to carry out tasks in a smart way Machine learning is a subset or a current application of AI It is based on the idea that machines should be able to access data and learn from it Deep learning is a subset of machine learning where similar machine learning algorithms are used to train deep neural networks to achieve better accuracy in cases where the former was not performing well Types of Machine Learning Machine learning can be categorized into three types Supervised Learning This is where you have input variables x and an output variable y and you use an algorithm to learn the mapping function from the input to the output y f x The goal is to approximate the mapping function 2 so well that whenever you have a new input data x you can predict the output variable y for that data Unsupervised Learning This is where you only have input data X and no corresponding output variables The goal for unsupervised learning is to model the underlying structure or distribution in the data to learn more about the data Reinforcement Learning This is where an agent learns to behave in an environment by performing certain actions and observing the rewards or punishments it gets for those actions Each type of machine learning is used in various domains such as banking healthcare and retail Supervised learning is a category of machine learning where the training data set is composed of labeled pictures which allows the model to recognize the ducts in the image The resulting predictive model can then be deployed to the production environment allowing it to recognize new pictures Popular supervised learning algorithms include linear regression random forest and support vector machines Common use cases include speech automation in mobile phones weather apps biometric attendance credit worthiness prediction in banking patient readmission rate prediction in healthcare and product analysis in retail Unsupervised learning on the other hand does not have any expected output associated with the training data set Instead the algorithm detects patterns based on the characteristics of the input data allowing it to group similar data instances together Popular unsupervised learning algorithms include k means apriori algorithm and hierarchical clustering Common use cases include customer segmentation in banking MRI data categorization in healthcare and product recommendation in retail 3 Finally reinforcement learning allows software agents and machines to automatically determine the ideal behavior within a speci c context to maximize its performance The learning agent interacts with the environment and leverages both exploration and exploitation mechanisms to improve its environment knowledge and select the next action Popular reinforcement learning algorithms include Q Learning and SARSA Common use cases include self driving cars gaming AI and robotics Reinforcement Learning In reinforcement learning an agent learns from its environment to take actions that maximize a reward The agent observes the environment selects an action using a policy and receives a reward or penalty The agent updates its policy to improve its decision making capabilities Reinforcement learning is used in various industries such as banking healthcare and retail In banking it is used to create a next best offer model for a call center In healthcare it is used to allocate medical resources for different types of ER In retail it can be used to reduce excess stock with dynamic pricing cases Data Science Data science is all about uncovering insights from data and making smarter business decisions It covers a wide spectrum of domains including arti cial intelligence machine learning and deep learning Arti cial intelligence is a subset of data science that lets machines simulate human like behavior Machine learning is a sub eld of arti cial intelligence that provides machines the ability to learn and improve from experiences without being explicitly programmed Deep learning is a part of machine 4 learning that uses computational measures and algorithms inspired by the structure and function of the human brain Recommendation Engines A recommendation engine lters down a list of choices for each user based on their browsing history ratings pro le details transactional details and more It provides every user a unique view of the ecommerce website based on their pro le and allows them to select relevant products Data science and machine learning are used to build a recommendation engine The data science lifecycle starts with de ning the business requirements and gathering data from different sources The next phase is data processing or cleaning which involves preparing the data for analysis The nal