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Pre requisite for the course, Data Structure and Algorithms, Engineering mathematics. (Probability), , Concept of programming using python.
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What you will learn from this course, Summarize the basic concepts artificial intelligence., Summarize the basic concepts data science., Illustrate & implement machine learning algorithms., Implement data pre-processing techniques., Describe statistical modeling techniques., Differentiate between supervised and unsupervised learning., Implement supervised learning algorithms., Implement unsupervised learning algorithms., Demonstrate Social networks as graphs., Identify Neighbourhood properties in graphs., Implement Dimensionality Reduction Techniques, Discuss privacy, security issue in data science., Define Next-generation data scientists
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SYLLABUS, , According to BTE-UP, , Subject: Machine Learning & Data Science, 1. Introduction to Data Science and Machine Learning, (14 Periods), Fundamentals of Artificial Intelligence,, Need and applications of Data Science,, Need and applications of Data Mining,, Need and applications of data preparation,, Need and applications of Machine Learning,, Types and Applications of Machine learning.
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2. Data Pre-processing, Analysis and Visualization, (10 Periods), Data Pre-processing: Pre-processing Techniques –, Mean Removal,, Scaling,, Normalization,, Binarization,, One Hot Encoding,, Label encoding,, , Data Analyses: Loading and summarizing the dataset,, Data Visualization:, Univariate Plots,, Multivariate Plots,, , Training Data, Test Data,, Performance Measures.
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3. Statistical Inference, , (12 Periods), , Populations and samples,, , Types of Statistical modelling,, Types of probability distributions., Parametric and Non-Parametric Methods,, Distance Metrics, , 4. Exploratory Data Analysis and the Data Science Process, (12 Periods), Basic tools (plots, graphs and summary statistics) of EDA,, , Philosophy of EDA, The Data Science Process
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5. Machine Learning Algorithms, Introduction to Supervised Learning Algorithms –, Decision Tree,, Linear Regression,, k-Nearest Neighbours (k-NN),, SVM (Support Vector Machine), , Introduction to Unsupervised Learning Algorithms –, K-means Clustering,, , MeanShift Algorithm,, , Dimensionality Reduction Techniques,, Introduction to Neural Networks,, , (12 Periods)
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6. Mining Social-Network Graphs, , (10 Periods), , Social networks as graphs,, , Clustering of graphs,, Direct discovery of communities in graphs,, Partitioning of graphs,, Neighbourhood properties in graphs, , 7. Data Science and Ethical Issues, Discussions on privacy, security, ethics,, A look back at Data Science,, Next-generation data scientists, , (16 Periods)
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LIST OF PRACTICALS, 1. WAP to implement the Decision Tree Algorithm, 2. WAP to implement the Linear Regression, 3. WAP to implement the k-Nearest Neighbours (k-NN), 4. WAP to implement the SVM Algorithm, 5. WAP to implement the K-means Clustering, 6. WAP to implement various Distance Metrics, 7. WAP to implement Dimensionality Reduction Techniques, , All practical's should be implemented in python language. As you all, have already gone through python course in previous semester.
