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Machine learning with Python
Introduction
Course preview (1:47)
Prospects of Machine learning (7:54)
Introduction to Machine Learning (9:54)
Course Curriculum (4:15)
Installation of jupyter notebook (10:56)
Python package Numpy for numerical computation (12:07)
Python package matplotlib for visualization (12:23)
Python package pandas for input and output (12:24)
Statistics and Exploratory Data Analysis
Brief introduction to Probability and Statistics (12:10)
Understanding different types of data (5:52)
Examining distribution of the variables (11:18)
Concept of Box Plot (4:39)
Examining relationship among variables (11:05)
Concept of Co-variance and Correlation (6:00)
Exploratory data analysis using python (8:27)
Regression analysis
Linear Regression on bi-variate data (8:57)
Python implementation of linear regression with bi-variate data (4:43)
Multivariate regression (12:18)
Python implementation of Gradient descent update rule for regression (11:40)
Advanced Topics: Normal Equation, Polynomial Regression and R-sq score (9:50)
Python implementation of linear regression with multivariate data in sklearn (4:13)
Python implementation of Polynomial Regression (9:44)
Logistic Regression Analysis
Classification problem and Classifier. Metrics for classification. (11:51)
Regression on Binary classification and multi-class classification problem (11:56)
Python program on GD update rule for logistic regression (11:43)
Python implementation of LR with binary and multiclass classification problem (8:16)
Other classification Algorithms
kNN classifier (7:44)
Implementation of kNN classifier using python (9:07)
Implementation of kNN classifier using python (9:05)
Support vector machine-II (5:48)
Implementation of SVM classifier using python (4:45)
Naïve Bayes Classifier (13:07)
Implementation of Naïve bayes classifier using python (5:33)
Decision Tree Classifier-I (13:05)
Implementation of Decision tree classifier using python (7:34)
Random Forest Classifier (5:10)
Implementation of RF classifier using python (5:12)
Dimensionality Reduction
Dimensionality and its problem. Linear algebra review: Eigen Value Decomposition (9:38)
Principal component analysis (12:41)
Principal component analysis in python (7:51)
Unsupervised Learning: Clustering
Unsupervised Learning (8:50)
k-Means clustering algorithm and its limitation (6:21)
Implementation of k-means clustering (8:35)
Hierarchical clustering (5:50)
Implementation of hierarchical clustering in python (5:39)
Artificial Neural Network
Perceptron and its learning rule, Limitations of perceptron (12:15)
ANN: Multilayered perceptron architecture (11:05)
ANN: Learning rule - BackProp contd. (10:47)
Build a ANN for hand digit recognition using ANN (8:14)
Build a ANN for hand digit recognition task in python (7:49)
Course preview