Course Content
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Introduction to ML 0/7
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Artificial Intelligence & Machine Learning IntroductionLecture1.1
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Who uses AI?Lecture1.2
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Supervised & Unsupervised LearningLecture1.3
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Regression & Classification ProblemsLecture1.4
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What makes a Machine Learning Expert?Lecture1.5
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What to learn to become a Machine Learning Developer?Lecture1.6
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Overview of Machine Learning AlgorithmsLecture1.7
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Linear Regression - Case Study & Project 0/7
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Regression Problem AnalysisLecture2.1
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Mathematical modelling of Regression ModelLecture2.2
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Gradient Descent AlgorithmLecture2.3
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Use casesLecture2.4
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Model SpecificationLecture2.5
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Building simple Univariate Linear Regression ModelLecture2.6
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Multivariate Regression ModelLecture2.7
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Decision Trees 0/5
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Forming a Decision TreeLecture3.1
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Mathematics of Decision TreeLecture3.2
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Decision Tree Evaluation for use casesLecture3.3
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Components of Decision TreeLecture3.4
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Ensemble of Decision Trees – Random forest, Bagging and BoostingLecture3.5
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Clustering 0/3
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K Means ClusteringLecture4.1
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Use Cases for K Means ClusteringLecture4.2
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Programming for K Means using PythonLecture4.3
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Machine Learning in Production 0/4
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Machine Learning pipelinesLecture5.1
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Deploying Machine Learning models to productionLecture5.2
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Hyper parameter tuningLecture5.3
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Accuracies, call backs and monitoringLecture5.4
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