Machine learning with Python
Naive Bayes Classifier, Decision tree, PCA, kNN classifier, linear regression, logistic regression,SVM classifier
Now a day’s Machine Learning is one of the most soughtafter skills in the industry. After completion of this course, students will understand and apply the concepts of machine learning and applied statistics for realworld problems.
Machine Learning is one of the most sought after skills in industry. After completion of this course students will understand and apply the concepts of machine learning and applied statistics for real world problems.
The topics we will be covering in this course are: Python libraries for data manipulation and visualization such as numpy, matplotlib and pandas. Linear Algebra, Exploratory Data Analysis, Linear Regression, Various Classification techniques, Clustering, Dimensionality reduction and Artificial Neural Networks.
This course is designed for Students who are pursuing bachelor’s or master’s degree in Statistics, Mathematics, Computer Science, Economics or any engineering fields. The students should have a little bit of knowledge in coding and undergraduate level mathematics.
Terminal competencies of the course, one would have learnt about tools to train machines based on realworld situations using Machine Learning algorithms, as well as to create complex algorithms and neural networks. During the latter stage of the course, learners will be introduced to realworld use cases of Machine Learning with Python for a HandsOn learning experience which would prepare them to create applications efficiently.
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Course Curriculum

PreviewCourse preview (1:47)

PreviewProspects of Machine learning (7:54)

PreviewIntroduction to Machine Learning (9:54)

StartCourse Curriculum (4:15)

StartInstallation of jupyter notebook (10:56)

StartPython package Numpy for numerical computation (12:07)

StartPython package matplotlib for visualization (12:23)

StartPython package pandas for input and output (12:24)

StartBrief introduction to Probability and Statistics (12:10)

StartUnderstanding different types of data (5:52)

StartExamining distribution of the variables (11:18)

StartConcept of Box Plot (4:39)

StartExamining relationship among variables (11:05)

StartConcept of Covariance and Correlation (6:00)

PreviewExploratory data analysis using python (8:27)

StartLinear Regression on bivariate data (8:57)

StartPython implementation of linear regression with bivariate data (4:43)

StartMultivariate regression (12:18)

StartPython implementation of Gradient descent update rule for regression (11:40)

StartAdvanced Topics: Normal Equation, Polynomial Regression and Rsq score (9:50)

StartPython implementation of linear regression with multivariate data in sklearn (4:13)

StartPython implementation of Polynomial Regression (9:44)