Lesson 1: CLO 1: Programming Skills in Python and R Programming
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Programming Skills in Python (Syntax, token, variables, statements, functions, Library, I/O systems, File management)
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R Programming Skills (Syntax, token, variables, statements, functions, Library, I/O systems, File management)
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Data Science methodologies and functions in Python and R Programming, Database concepts
Lesson 2: CLO 2: Application of Statistical concepts such as probability, inference, and modeling in practice
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Lesson 3: CLO 3: Learning of Data visualization with tidyverse, ggplot2 and data wrangling tools with dplyr
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Implementation of methods:
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tidyverse
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ggplot2
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dplyr
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pandas
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scikit-learn
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statsmodels
Lesson 4: CLO 4: Implementing Machine Learning/Deep Learning practicing tools under Unix/Linux, git and GitHub, and RStudio
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Machine Learning using Unix/Linux, git and GitHub, and RStudio
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Understand parametric and non-parametric algorithms, clustering, dimensionality reduction, among other important topics.
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Real-world based case studies on algorithms to build smart robots, understand text, audio, database mining.
Deep Learning using Unix/Linux, git and GitHub, and RStudio
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Learn about convolutional networks, RNNs, BatchNorm, Dropout and more.
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Different techniques to solve real-life problems
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Real-world case studies in fields such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing are covered
Lesson 5: CLO 5: Optimization of problems with a Realtime project
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Optimization techniques in algorithms
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Asymptotic Analysis
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Case Study (Real time)