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Summary: The blog explores the synergy between ArtificialIntelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction ArtificialIntelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.
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 artificialintelligence 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.
A/B Testing: A statistical method for comparing two versions of a variable to determine which one performs better. ArtificialIntelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence.
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)?
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