This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Machine learning is a field of computer science that uses statistical techniques to build models from data. The ability to understand the principles of probability, hypothesistesting, and confidence intervals enables data scientists to validate their findings and ascertain the reliability of their analyses.
Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesistesting, regression analysis, and experimental design, is paramount in Data Science roles. Examples include linear regression, logistic regression, and supportvectormachines.
HypothesisTesting : Statistical Models help test hypotheses by analysing relationships between variables. These models help in hypothesistesting and determining the relationships between variables. Bayesian models and hypothesistests (like t-tests or chi-square tests) are examples of inferential models.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. SupportVectorMachines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane.
Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesistesting, regression analysis, and descriptive statistics. Machine Learning Algorithms Basic understanding of Machine Learning concepts and algorithm s, including supervised and unsupervised learning techniques.
Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and supportvectormachines.
Inferential Statistics: A branch of statistics that makes inferences about a population based on a sample, allowing for hypothesistesting and confidence intervals. S Supervised Learning: A type of Machine Learning where the model is trained on labelled data, learning to predict outcomes based on input features.
Hypothesistesting and regression analysis are crucial for making predictions and understanding data relationships. Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and supportvectormachines.
Another example can be the algorithm of a supportvectormachine. Hence, we have various classification algorithms in machine learning like logistic regression, supportvectormachine, decision trees, Naive Bayes classifier, etc. What are SupportVectors in SVM (SupportVectorMachine)?
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content