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Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesistesting, regression analysis, and experimental design, is paramount in Data Science roles. Differentiate between supervised and unsupervised learning algorithms.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. These techniques span different types of learning and provide powerful tools to solve complex real-world problems.
Students should learn about data wrangling and the importance of data quality. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesistesting, regression analysis, and descriptive statistics. Students should learn how to train and evaluate models using large datasets.
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Explore MachineLearning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervisedlearning such as linear regression , logistic regression, decision trees, and supportvectormachines.
Hypothesistesting and regression analysis are crucial for making predictions and understanding data relationships. MachineLearningSupervisedLearning includes algorithms like linear regression, decision trees, and supportvectormachines.
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