Course curriculum

  • 1

    Welcome to the course!

    • About this course: Overview, Learning Outcomes, Who Should Enroll...
    • Instructor bio - Pramod Gupta
    • Course content noted as "Pending" will be published after it streams live, on air
  • 2

    Module 1

    • Module 1 - Introduction to Python, Libraries and Installation
    • Module 1 - SLIDES
    • Segment - 01 - Introduction
    • Segment - 02 - A bit about artificial intelligence and machine learning
    • Segment - 03 - Software choices for AI and ML
    • Segment - 04 - Data Mining process
    • Segment - 05 - What is data and data analysis?
    • Segment - 06 - Data quality and its measure
    • Segment - 07 - Data analysis
    • Segment - 08 - Installing Python, Running Juypter Notebook
    • Segment - 09 - Data analytics philosophy and teaching
  • 3

    Module 2

    • Module 2 - Data Structures in Python, Lists and Tuples and Various Operations
    • Segment - 10 - Python Basics
    • Segment - 11 - Strings
    • Segment - 12 - Other Functions and Help
    • Segment - 13 - Data Structures and Lists
    • Segment - 14 - List Comprehension
    • Segment - 15 - Tuple Data Structure
  • 4

    Module 3

    • Module 3 - Data Structures in Python, Dictionaries and Sets and Various Operations
    • Segment - 16 - Dictionary and Data Structures
    • Segment - 17 - Sets
    • Segment - 18 - Set Comprehension and Control Flow
    • Segment - 19 - NumPy and-SciPy
  • 5

    Module 4

    • Module 4 - Introduction to NumPy, 1D Array and 2D Arrays
    • Segment - 20 - NumPy in Depth
    • Segment - 21 - Indexing
  • 6

    Module 5

    • Module 5 - Deep Dive into NumPy and Various Operations with Arrays
    • Segment - 22 - Array Operations and Mathematics
    • Segment - 23 - Sorting Arrays
    • Segment - 24 - Broadcasting
    • Segment - 25 - Dot Matrices
    • Segment - 26 - logical-operations
    • Segment - 27 - Saving NumPy to CSV Files
    • Segment - 28 - Introduction to Pandas
  • 7

    Module 6

    • Module 6 - Introduction to Pandas, Pandas Series and Various Operations on Series
    • Segment - 29 - Pandas
    • Segment - 30 - Pandas Series
    • Segment - 31 - operations-on-series
    • Segment - 32 - Arithmetic Operations
    • Segment - 33 - Sorting
    • Segment - 34 - Data Cleaning
  • 8

    Module 7

    • Module 7 - Intro to Pandas DataFrame and Various Operations with DataFrames
    • Segment - 35 - Dataframes
    • Segment - 36 - Further Dataframe Operations
    • Segment - 37 - Still More Dataframe Operations
  • 9

    Module 8

    • Module 8 - Data Cleaning and Transformation with Pandas
    • Segment - 38 - Data Cleaning and Transformation
    • Segment - 39 - Manor Tasks in Data Transformation
    • Segment - 40 - DataFrames and Transforming Data
    • Segment - 41 - Dealing with Missing Data
    • Segment - 42 - Discretization and Binning
    • Segment - 43 - Detecting and Filtering Outliers
  • 10

    Module 9

    • Module 9 - Data Visualization with Matplotlib/Seaborn
    • Segment - 44 - Data Visualization Foundations
    • Segment - 45 - Basic Data Visualization Principles
    • Segment - 46 - Selecting the Right Chart Type and Tools
    • Segment - 47 - Types and Uses of Graphs
    • Segment - 48 - Matplotlib
  • 11

    Module 10

    • Module 10 - Introduction to Machine Learning and Scikit-Learn
    • Segment - 49 - The Basics of Machine Learning
    • Segment - 50 - Steps to Applying Machine Learning
    • Segment - 51 - Machine Learning Paradigms
    • Segment - 52 - Why Machine Learning and Why Now
    • Segment - 53 - Measuring Model Performance
    • Segment - 54 - Scikit-Learn Introduction
  • 12

    Labs

    • Labs - Overview
    • Instructions: Using Jupyter Notebooks
    • Instructions: Virtual labs (For Colaboratory)
    • Labs: Module 1