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Summary: A comprehensive BigData syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of BigData Understanding the fundamentals of BigData is crucial for anyone entering this field.
While data science and machinelearning are related, they are very different fields. In a nutshell, data science brings structure to bigdata while machinelearning focuses on learning from the data itself. What is data science?
MachineLearning Algorithms Candidates should demonstrate proficiency in a variety of MachineLearning algorithms, including linear regression, logistic regression, decision trees, random forests, supportvectormachines, and neural networks.
These techniques span different types of learning and provide powerful tools to solve complex real-world problems. SupervisedLearningSupervisedlearning is one of the most common types of MachineLearning, where the algorithm is trained using labelled data.
e) BigData Analytics: The exponential growth of biological data presents challenges in storing, processing, and analyzing large-scale datasets. Traditional computational infrastructure may not be sufficient to handle the vast amounts of data generated by high-throughput technologies.
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.
Association Rule Learning: A rule-based MachineLearning method to discover interesting relationships between variables in large databases. B BigData : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis.
Instead of memorizing the training data, the objective is to create models that precisely predict unobserved instances. Supervised, unsupervised, and reinforcement learning : Machinelearning can be categorized into different types based on the learning approach.
Several technologies bridge the gap between AI and Data Science: MachineLearning (ML): ML algorithms, like regression and classification, enable machines to learn from data, enhancing predictive accuracy.
This capability bridges various disciplines, leveraging techniques from statistics, machinelearning, and artificial intelligence. Some key areas include: BigData analytics: It helps in interpreting vast amounts of data to extract meaningful insights.
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