Machine design data book

machine design data book k.mahadevan and k.balaveera reddy very useful for both design 1 and 2

Design data book for Mechanical Engineering students in good condition.

DATA BOOK FOR MECHANICAL ENGINEERING STUDENTS URGENT SELL PLEASE CALL

machine design data handbook volume 1 and 2 by k lingaiah, in pristine condition (original book) not used, shipping all over india (minimum postal charges to be paid by buyer

design data book for mechanical branch b.tech,writter k.Mahadevan (mo.no 83.73.94.60.67 near ITS Mohan nagar)

PSG Design Data Book for B.E Mechanical Engineering students I used to refer this book for Design of Machine Elements (DME) subject. It is kept in good condition.

Machine design book for DMM-1 & DMM-2 SUBJECTS in Mechanical stream

This is not the design data book. It is the textbook that can be used to get conceptually sound in designing of various mechanical elements. It is a book in good condition.

This book is only for college students who want to study Design of Machine Elements

Machine learning is A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. It is very difficult to categorize to all the decisions based on all possible inputs. So to solve this problem, algorithms are developed. These algorithms are developed based on specific data and past experience with the principles of statistics and probability theory, logic,search. The developed algorithms form the basis of various applications such as: •Vision processing •Language processing •Forecasting (e.g., stock market trends) •Pattern recognition •Games •Data mining •Expert systems •Robotics SOME MACHINE LEARNING METHODS Machine learning algorithms are often categorized as I.Supervised machine learning algorithms II.unsupervised machine learning algorithms III.Semi-supervised machine learning algorithms IV.Reinforcement machine learning algorithms Supervised machine learning algorithms: Supervised learning deals with learning a function from available training data. A supervised learning algorithm analyzes the training data and produces an inferred function. Some common examples are, •classifying e-mails as spam, •labeling web pages based on their content, and •voice recognition. There are some supervised algorithms such as neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers. Unsupervised machine learning algorithms: Unsupervised learning is an extremely powerful tool for analyzing available data and look for patterns and trends. It is most commonly used for clustering similar input into logical groups. There are some common approaches such as, •k-means •self-organizing maps, and •hierarchical clustering Semi-supervised machine learning algorithms: Semi-supervised is comes between supervised and unsuperv

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