Machine Learning and Data Science Using Python

Module-1​Welcome to the Pre-Program Preparatory ContentSession-1:​1) Introduction​2) Preparatory Content Learning ExperienceMODULE-2​INTRODUCTION TO PYTHONSession-1:​Understanding Digital Disruption Course structure​1) Introduction​2) Understanding Primary Actions​3) Understanding es & Important PointersSession-2:​Introduction to python​1) Getting Started — Installation​2) Introduction to Jupyter Notebook​The Basics Data Structures in Python3) Lists​4) Tuples​5) Dictionaries​6) SetsSession-3:​Control Structures and Functions​1) Introduction​2) If-Elif-Else​3) Loops​4) Comprehensions​5) Functions​6) Map, Filter, and Reduce​7) SummarySession-4:​Practice Questions​1) Practice Questions I​2) Practice Questions IIModule-3​Python for Data ScienceSession-1:​Introduction to NumPy​1) Introduction​2) NumPy Basics​3) Creating NumPy Arrays​4) Structure and Content of Arrays​5) Subset, Slice, Index and Iterate through Arrays​6) Multidimensional Arrays​7) Computation Times in NumPy and Standard Python Lists​8) SummarySession-2:​Operations on NumPy Arrays​1) Introduction​2) Basic Operations​3) Operations on Arrays​4) Basic Linear Algebra Operations​5) SummarySession-3:​Introduction to Pandas​1) Introduction​2) Pandas Basics​3) Indexing and Selecting Data​4) Merge and Append​5) Grouping and Summarizing Data frames​6) Lambda function & Pivot tables​7) SummarySession-4:​Getting and Cleaning Data​1) Introduction2) Reading Delimited and Relational Databases​3) Reading Data from Websites​4) Getting Data from APIs​5) Reading Data from PDF Files​6) Cleaning Datasets​7) SummarySession-5:​Practice Questions​1) NumPy Practice Questions​2) Pandas Practice Questions​3) Pandas Practice Questions SolutionModule-4Session-1:​Vectors and Vector Spaces​1) Introduction to Linear Algebra​2) Vectors: The Basics​3) Vector Operations - The Dot Product​4) Dot Product - Example Application​5) Vector Spaces​6) SummarySession-2:​Linear Transformations and Matrices​1) Matrices: The Basics​2) Matrix Operations - I​3) Matrix Operations - II4) Linear Transformations​5) Determinants​6) System of Linear Equations​7) Inverse, Rank, Column and Null Space​8) Least Squares Approximation​9) SummarySession-3:​Eigenvalues and Eigenvectors​1) Eigenvectors: What Are They?​2) Calculating Eigenvalues and Eigenvectors​3) Eigen decomposition of a Matrix​4) SummarySession-4:​Multivariable CalculusModule-5Session-1:​Introduction to Data Visualisation​1) Introduction: Data Visualisation​2) Visualisations - Some Examples​3) Visualisations - The World of Imagery​4) Understanding Basic Chart Types I​5) Understanding Basic Chart Types II​6) Summary: Data VisualisationSession-2:​Basics of Visualisation Introduction​1) Data Visualisation Toolkit​2) Components of a Plot​3) Sub-Plots​4) Functionalities of Plots​5) SummarySession-3:​Plotting Data Distributions Introduction​1) Univariate Distributions​2) Univariate Distributions - Rug Plots​3) Bivariate Distributions​4) Bivariate Distributions - Plotting Pairwise Relationships​5) SummarySession-4:​Plotting Categorical and Time-Series Data​1) Introduction​2) Plotting Distributions Across Categories​3) Plotting Aggregate Values Across Categories​4) Time Series Data​5) SummarySession-5:​1) Practice Questions I​2) Practice Questions II