Course Content
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Day 01 - Module-1 Introduction to ML 0/8
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Artificial Intelligence & Machine Learning IntroductionLecture1.1
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Who uses AI?Lecture1.2
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AI and ML for telecomLecture1.3
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Supervised & Unsupervised LearningLecture1.4
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Regression & Classification ProblemsLecture1.5
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What makes a Machine Learning Expert?Lecture1.6
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What to learn to become a Machine Learning Developer?Lecture1.7
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Overview of Machine Learning AlgorithmsLecture1.8
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Module-2 Introduction to Python 0/4
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Basic syntaxLecture2.1
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Data structures in PythonLecture2.2
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FunctionsLecture2.3
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Object oriented ProgrammingLecture2.4
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Module-3 Data Structure & Data Manipulation 0/9
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Indexing, Data Processing using Numpy Arrays and PandasLecture3.1
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Mathematical computing basicsLecture3.2
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Basic statisticsLecture3.3
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File Input and OutputLecture3.4
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Getting Started with DataframesLecture3.5
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Data Acquisition (Import & Export)Lecture3.6
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Selection and FilteringLecture3.7
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Combining and Merging Data FramesLecture3.8
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Removing Duplicates & String ManipulationLecture3.9
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Module-4 Data Visualization 0/6
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Data pre processingLecture4.1
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Data cleaning and data transformationLecture4.2
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Feature extractionLecture4.3
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Recursive feature eliminationLecture4.4
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Feature transformationLecture4.5
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Discussion on mining massive data setsLecture4.6
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Day 02 - Module-1 Linear Regression - Case Study & Project 0/7
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Regression Problem AnalysisLecture5.1
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Mathematical modelling of Regression ModelLecture5.2
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Gradient Descent AlgorithmLecture5.3
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Use casesLecture5.4
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Regression TableLecture5.5
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Building simple Univariate Linear Regression ModelLecture5.6
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Multivariate Regression ModelLecture5.7
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Module-2 Linear Regression - 2 0/3
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Polynomial Linear RegressionLecture6.1
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Ridge RegressionLecture6.2
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Lasso RegressionLecture6.3
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Module-3 Logistic Regression 0/13
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AssumptionsLecture7.1
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Reason for the Logit TransformLecture7.2
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Logit TransformationLecture7.3
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HypothesisLecture7.4
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Variable and Model SignificanceLecture7.5
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Maximum Likelihood ConceptLecture7.6
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Log Odds and InterpretationLecture7.7
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Null Vs Residual DevianceLecture7.8
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Chi Square TestLecture7.9
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ROC CurveLecture7.10
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Model SpecificationLecture7.11
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Confusion MatrixLecture7.12
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Accuracy, Recall, Precision and F1 ScoreLecture7.13
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Day 03 - Module-1 Decision Trees 0/4
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Forming a Decision TreeLecture8.1
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Components of Decision TreeLecture8.2
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Mathematics of Decision TreeLecture8.3
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Decision Tree Evaluation for use casesLecture8.4
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Module-2 Random Forests 0/2
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Random Forest MathematicsLecture9.1
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Examples & use cases using Random ForestsLecture9.2
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Module-3 KNN 0/3
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KNN TheoryLecture10.1
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KNN with PythonLecture10.2
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KNN Exercise with real dataLecture10.3
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Module-4 Bagging and Boosting 0/5
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Bias Variance TradeoffsLecture11.1
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BaggingLecture11.2
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BoostingLecture11.3
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BootstrappingLecture11.4
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Ensemble models with real world dataLecture11.5
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Day 04 - Module-1 Naïve Bayes Algorithm 0/8
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Bayesian TheoremLecture12.1
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Probabilities – The Prior and Posterior ProbabilitiesLecture12.2
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Conditional and Joint Probabilities NotionLecture12.3
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Traditional Approach – Extract Important Features 0 question10 minQuiz12.1
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Naive Approach – Independence of Features Assumption 0 question10 minQuiz12.2
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Data Processing – Discretization of Features 0 question10 minQuiz12.3
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Advantages & Disadvantages of Naïve Bayes Models 0 question10 minQuiz12.4
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Examples, Projects & Case Studies 0 question10 minQuiz12.5
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Module-2 Support Vector Machine 0/4
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Concept and Working PrincipleLecture13.1
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Mathematical ModellingLecture13.2
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The Kernel Method and Nonlinear HyperplanesLecture13.3
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Use Cases & Programming SVM using PythonLecture13.4
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Module-3 Recommendation Systems 0/2
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Market basket AnalysisLecture14.1
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Collaborative FilteringLecture14.2
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Day 05 - Module-1 Clustering 0/5
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Hierarchical ClusteringLecture15.1
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K Means ClusteringLecture15.2
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Use Cases for K Means ClusteringLecture15.3
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Programming for K Means using PythonLecture15.4
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Cluster Size Optimization vs Definition OptimizationLecture15.5
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Module-2 Time Series 0/9
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Definition of Time SeriesLecture16.1
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Time Series DecompositionLecture16.2
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Simple Moving Average MethodLecture16.3
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Weighted Moving Average MethodLecture16.4
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Single Exponential Smoothing MethodLecture16.5
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Double Exponential Smoothing MethodLecture16.6
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Triple Exponential Smoothing MethodLecture16.7
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Stationarity of DataLecture16.8
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ARIMA ModelsLecture16.9
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Module-3 Reinforcement Learning 0/3
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Upper Confidence BoundsLecture17.1
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Thompson SamplingLecture17.2
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Example and use casesLecture17.3
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Day 06 - Module-1 Artificial Neural Networks 0/8
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Activation Functions for Neural NetworksLecture18.1
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Optimization Techniques – SGD, ADAM, LBFGSLecture18.2
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RegularizationLecture18.3
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Momentum in Neural NetworksLecture18.4
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Neural Network Tuning and Performance OptimizationLecture18.5
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Perceptrons, Multilayer Perceptrons, Weights, Biases,Backpropagation,OptimizationLecture18.6
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Training, Validation and TestingLecture18.7
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End-to-end development of a multiclass classification problem in Python/MATLABLecture18.8
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Module-2 Convolutional Neural Networks 0/7
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CNN ArchitectureLecture19.1
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Introduction to Deep Learning, History, ApplicationsLecture19.2
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Shallow Networks and Deep NetworksLecture19.3
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Convolutional Neural Networks, Pooling layers, Fully Connected layers, Activation functions, ReLU, Dropout, loss function and Optimization with respect to CNNLecture19.4
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Introduction to frameworks Tensorflow, KerasLecture19.5
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Image classification using convolutional neural networks, visualisation of featuresLecture19.6
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MNIST Data – Digit Classification using CNNLecture19.7
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Module-3 Recurrent and Recursive Nets 0/4
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Basic concepts of RNNLecture20.1
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The Vanishing Gradient ProblemLecture20.2
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LSTM NetworksLecture20.3
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Recursive Neural NetworksLecture20.4
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Module-4 Machine learning at scale 0/3
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AWS ML OfferingsLecture21.1
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Google mL OfferingsLecture21.2
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On- Premised DeploymentsLecture21.3
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Regression Table