07.7 - Machine Learning - Classifiers - Regression and Classification algorithms
01.1 - Scikit-learn - Machine Learning in Python
02.2 - Machine Learning - Colab notebooks- Jupyter Data science
03.3 - Machine Learning - Supervised learning and unsupervised learning
04.4 - Machine Learning - training model and testing process
05.5 - Machine Learning - Confusion matrix
06.6 - Machine Learning - Dataset
07.7 - Machine Learning - Classifiers - Regression and Classification algorithms
08.8 - Linear regression - Theory - Simple linear regression
09.9 - Simple Linear regression - Implementation
10.10 - Multiple linear regression - Theory & implementation
11.11 - Polynomial regression - Theory & implementation
12.12 - Logistic Regression - Theory & implementation
13.13 - Naive Bayes Classifier in Machine Learning - Theory
14.14 - Naive Bayes Classifier in Machine Learning - Implementation
15.15 - DecisionTreeClassifier - Theory & implementation
16.16 - Support Vector Machines (SVM) Classifier - Theory & implementation
17.17 - K-Nearest Neighbor(KNN) Algorithm - Theory & implementation
18.18 - Artificial neural networks ( ANN ) - Multi-layer Perceptron classifier (MLP) - Theory
19.19 - Artificial neural networks ( ANN ) - Multi-layer Perceptron classifier (MLP) - Implementation
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