The course is open to students of engineering colleges, technical institutions and industry people
The Signal Processing group of the School of ECE is organising a certification course on “Problem-solving using AIML techniques” starting from October 2023. Experts from the School of ECE and industry will deliver lectures with hands-on sessions. The total contact hours of the course will be 40 hours. The course focuses more on practical sessions (30 hours) with the required theory sessions (12 hours) followed by the assessment. After successful completion of the course, the candidates will be awarded the certificate.
The goal of this certification course is to give a firm foundation in the implementation of Machine Learning algorithms. The course focuses on the conceptual depths of topics such as Data Science, Machine Learning and Artificial Intelligence, The course imparts extensive knowledge on Python for Data Science, Data Visualization in Python, Exploratory Data Analysis, Linear Regression, Logistic Regression, Classification using Decision Trees, unsupervised learning: clustering, principal component analysis.
|1||Python for Data Science Data Visualization in Python||Introduction to NUMPY, Introduction to matplotlib, Introduction to PANDAS, Getting and Cleaning Data. Introduction to Data visualisation, Data visualization using seaborn|
|2||Exploratory Data Analysis||Data Cleaning, Univariate Analysis, Bivariate Analysis, Multivariate Analysis, Credit EDA Case Study|
|3||Linear Regression Logistic Regression||
Simple Linear Regression, Simple Linear Regression In Python, Multiple Linear Regression, Multiple Linear Regression In Python, Industry Relevance Of Linear Regression.
Univariate Logistic Regression, Multivariate Logistic Regression: Model Building and Evaluation, Logistic Regression: Industry Applications
|4||Classification using Decision Trees||Introduction to Decision Trees, Algorithms for Decision Trees Construction, Hyperparameter Tuning In Decision Trees|
|5||Unsupervised Learning: Clustering||Introduction To Clustering, K-Means Clustering, Hierarchical Clustering, Other Forms of Clustering: K-Mode, K-Prototype, DB Scan|
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