Machine Learning and Data Science: Fundamental, Challenges and Future

Speaker:  Ujjwal Maulik – Kolkata, India
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Supervised and unsupervised pattern classification are important Machine Learning techniques which have wide range of applications. While supervised classification techniques use training samples to find the class information of test samples, unsupervised classification partitioning a given data set into homogeneous groups based on some similarity/dissimilarity metric. 

In this lecture first we try to understand some basic supervised and unsu
pervised machine leaning algorithms like KNN, Decision Tree, Bayes classifier, K Mean, FCM, will be discussed along with suitable example. In the second part of the lecture we will focus about the basic issues and challenges in data science.  We will start from simple to complex data and finally discuss about big data analysis. We will discuss about how to use new architecture like Spark to handle large volume of data. Some real life examples will also be provided.  

About this Lecture

Number of Slides:  60
Duration:  120 minutes
Languages Available:  English
Last Updated: 

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