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Introduction to Python
Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
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Overview of PythonLecture1.1
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The Companies using PythonLecture1.2
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Different Applications where Python is usedLecture1.3
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Discuss Python Scripts on UNIX/WindowsLecture1.4
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Values, Types, VariablesLecture1.5
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Operands and ExpressionsLecture1.6
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Conditional StatementsLecture1.7
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LoopsLecture1.8
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Command Line ArgumentsLecture1.9
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Writing to the screenLecture1.10
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Sequences and File Operations
Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
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Python files I/O FunctionsLecture2.1
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NumbersLecture2.2
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Strings and related operationsLecture2.3
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Tuples and related operationsLecture2.4
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Lists and related operationsLecture2.5
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Dictionaries and related operationsLecture2.6
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Sets and related operationsLecture2.7
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Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
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FunctionsLecture3.1
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Function ParametersLecture3.2
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Global VariablesLecture3.3
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Variable Scope and Returning ValuesLecture3.4
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Lambda FunctionsLecture3.5
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Object-Oriented ConceptsLecture3.6
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Standard LibrariesLecture3.7
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Modules Used in PythonLecture3.8
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The Import StatementsLecture3.9
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Module Search PathLecture3.10
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Package Installation WaysLecture3.11
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Errors and Exception HandlingLecture3.12
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Handling Multiple ExceptionsLecture3.13
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Introduction to NumPy, Pandas and Matplotlib
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
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NumPy – arraysLecture4.1
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Operations on arraysLecture4.2
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Indexing slicing and iteratingLecture4.3
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Reading and writing arrays on filesLecture4.4
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Pandas – data structures & index operationsLecture4.5
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Reading and Writing data from Excel/CSV formats into PandasLecture4.6
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matplotlib libraryLecture4.7
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Grids, axes, plotsLecture4.8
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Markers, colours, fonts and stylingLecture4.9
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Types of plots – bar graphs, pie charts, histogramsLecture4.10
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Contour plotsLecture4.11
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Data Manipulation
Learning Objective: Through this Module, you will understand in detail about Data Manipulation
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Basic Functionalities of a data objectLecture5.1
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Merging of Data objectsLecture5.2
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Concatenation of data objectsLecture5.3
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Types of Joins on data objectsLecture5.4
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Exploring a DatasetLecture5.5
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Analysing a datasetLecture5.6
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Introduction to Machine Learning with Python
Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
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Python Revision (numpy, Pandas, scikit learn, matplotlib)Lecture6.1
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What is Machine Learning?Lecture6.2
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Machine Learning Use-CasesLecture6.3
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Machine Learning Process FlowLecture6.4
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Machine Learning CategoriesLecture6.5
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Linear regressionLecture6.6
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Gradient descentLecture6.7
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Supervised Learning - I
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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What are Classification and its use cases?Lecture7.1
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What is Decision Tree?Lecture7.2
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Algorithm for Decision Tree InductionLecture7.3
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Creating a Perfect Decision TreeLecture7.4
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Confusion MatrixLecture7.5
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What is Random Forest?Lecture7.6
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Dimensionality Reduction
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
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Introduction to DimensionalityLecture8.1
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Why Dimensionality ReductionLecture8.2
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PCALecture8.3
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Factor AnalysisLecture8.4
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Scaling dimensional modelLecture8.5
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LDALecture8.6
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Supervised Learning - II
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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What is Naïve Bayes?Lecture9.1
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How Naïve Bayes works?Lecture9.2
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Implementing Naïve Bayes ClassifierLecture9.3
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What is Support Vector Machine?Lecture9.4
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Illustrate how Support Vector Machine works?Lecture9.5
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Hyperparameter OptimizationLecture9.6
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Grid Search vs Random SearchLecture9.7
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Implementation of Support Vector Machine for ClassificationLecture9.8
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Unsupervised Learning
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
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What is Clustering & its Use Cases?Lecture10.1
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What is K-means Clustering?Lecture10.2
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How does K-means algorithm work?Lecture10.3
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How to do optimal clusteringLecture10.4
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What is C-means Clustering?Lecture10.5
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What is Hierarchical Clustering?Lecture10.6
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How Hierarchical Clustering works?Lecture10.7
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Association Rules Mining and Recommendation Systems
Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
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What are Association Rules?Lecture11.1
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Association Rule ParametersLecture11.2
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Calculating Association Rule ParametersLecture11.3
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Recommendation EnginesLecture11.4
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How does Recommendation Engines work?Lecture11.5
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Collaborative FilteringLecture11.6
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Content-Based FilteringLecture11.7
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Reinforcement Learning
Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
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What is Reinforcement LearningLecture12.1
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Why Reinforcement LearningLecture12.2
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Elements of Reinforcement LearningLecture12.3
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Exploration vs Exploitation dilemmaLecture12.4
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Epsilon Greedy AlgorithmLecture12.5
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Markov Decision Process (MDP)Lecture12.6
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Q values and V valuesLecture12.7
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Q – LearningLecture12.8
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α valuesLecture12.9
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Time Series Analysis
Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
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What is Time Series Analysis?Lecture13.1
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Importance of TSALecture13.2
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Components of TSALecture13.3
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White NoiseLecture13.4
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AR modelLecture13.5
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MA modelLecture13.6
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ARMA modelLecture13.7
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ARIMA modelLecture13.8
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StationarityLecture13.9
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ACF & PACFLecture13.10
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Model Selection and Boosting
Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
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What is Model Selection?Lecture14.1
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The need for Model SelectionLecture14.2
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Cross-ValidationLecture14.3
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What is Boosting?Lecture14.4
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How Boosting Algorithms work?Lecture14.5
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Types of Boosting AlgorithmsLecture14.6
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Adaptive BoostingLecture14.7
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