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Machine Learning and Data Science Using Python - Part 1
Rating: 4.4 out of 5(33 ratings)
7,060 students

Machine Learning and Data Science Using Python - Part 1

Begin your Machine Learning and Data Science Journey
Created byDigital Disruption Pte. Ltd.
Last updated 12/2021
English
English [Auto],

What you'll learn

  • Introduction to Python
  • Data Structures in Python
  • Control Structures and Functions
  • Python for Data Science
  • Introduction to NumPy
  • Operations on NumPy Arrays
  • Introduction to Pandas
  • Getting and Cleaning Data
  • Data Visualisation in Python
  • Introduction to Data Visualisation
  • Basics of Visualisation
  • Plotting Data Distributions
  • Plotting Categorical and Time-Series Data

Course content

1 section • 40 lectures • 3h 35m total length

  • Introduction3:10

    Explore the machine learning and data science curriculum, starting with Python basics, control structures, and functions. Advance through data visualization and data analysis across modules and sessions.

  • Welcome to the Pre-Program Preparatory Content4:31

    The preparatory course builds a solid foundation in Python for data analytics and statistics, guiding you through programming and data visualization.

  • Introduction to AI, ML, DL & DS15:52

    • Artificial Intelligence (AI): Enables The Machine to think. DL and ML are used to derive an AI application

      • Machine Learning (ML): Statistical Tools to Explore and Analyze the Data

        • Supervised

          • Past Labeled Data

        • Unsupervised

          • Clustering

        • Reinforcement

        • Semi supervised (Learns from Environment)

      • Deep Learning (DL): Multi Neural Network Architecture to mimic human brain

        • ANN (Artificial Neural Network) : When data presents in the form of Numbers

        • CNN (Convolution Neural Network) : When the input is in the form of Images

          • Transfer Learning (TL)

      • RNN (Recurrent Neural Network) : When input in in the form of Time series data

      • Data Science (DS): Statistics, Probability and Linear Algebra

  • Machine Learning and Data Science Roadmap8:06

    Navigate a practical roadmap for starting machine learning and data science, from probabilities to Python libraries. Grasp supervised and unsupervised learning, deep learning, neural networks, and the data-driven AI landscape.

  • The Best Realtime Datasets5:40

    1) Private Dataset Sources

    Usually, you get from the client. Example of the private data

    •Telecom data,

    •Retail data,

    •Banking Data and

    •Medical data, etc.


    2) Public Dataset Sources


    2.1) Government Public Datasets:


    •Government Datasets : (General format data.gov.<Countries Shortcut>)

    •US Government Datasets: https://www.data.gov/

    •India Government Datasets https://data.gov.in/

    •Singapore Government Datasets https://data.gov.sg/ .etc.


    2.2) Non-Government Public Datasets:

    •https://www.kaggle.com/

    •https://archive.ics.uci.edu/ml/index.php

    •https://github.com/awesomedata/awesome-public-datasets

    •https://github.com/datameet


  • Transition to Data Science Role11:43
  • Anaconda Installation5:36

    Install Anaconda, the free open-source Python distribution for data science, using the Individual Edition for your OS. Launch Anaconda Navigator and Jupyter Notebook to start projects.

  • Introduction to Python0:42

    Explore an introduction to Python in module two, covering digital disruption, a coding council session, and a focus on control structures and functions.

  • Jupyter Notebook Introductory Session (Shortcuts)6:14

    Learn Jupyter notebook shortcuts and cell modes, convert cells between code and markdown, and run your first Python program with input and print while formatting text with Markdown and LaTeX.

  • Jupyter Note Book Shortcuts
  • Data Structures in Python4:08

    Explore Python data structures—lists, dictionaries, and sets—and compare mutable and immutable containers. Learn how these structures organize data across web, AI, and software development.

  • Python Code: Introduction-DataTypes3:00
  • Recollect Data Types
  • Python Code : Lists5:16
  • Recap Lists
  • Python Code - Tuples1:23
  • Recap Tuples
  • Python Code : Dictionaries3:23

    Explore dictionaries as ordered key-value data maps, learn to create and update entries, check for keys, merge, delete, and list all keys and values.

  • Recap Dictionaries
  • Python Code : Sets1:16

    Convert a list to a Python set to enforce uniqueness, explore set mutability and non-duplicate elements, and learn to perform unions to combine sets.

  • Recap Sets
  • Control Structures and Functions4:41

    Explore Python control structures and functions, including if statements, for and while loops, dictionary and list comprehensions, and map, reduce, filter, plus writing your own functions.

  • Python Code : Conditional - IfElse1:50

    Master conditional statements and if-else in Python to classify scores, filter strings by keywords, and perform basic sentiment analysis; explore even, odd, and divisibility checks.

  • Recap IfElse
  • Python Code : Looping-For While2:10

    Learn to implement Python looping with for and while, using range, enumerate, and conditional checks on strings and lists, while working with dictionaries to access keys and values.

  • Recap Looping
  • Python Code: Comprehensions2:03
  • Recap Comprehensions
  • Python Code : Functions1:44

    Explore Python functions, including defining functions, passing parameters, printing squares and factorials, using variable-length arguments with *args, and applying lambda expressions for one-line functions.

  • Recap Functions
  • Python Code : Map, Reduce & Filter1:16

    Explore map, reduce, and filter in Python with examples, including squaring list elements with map, joining lists, filtering by divisibility by three, and using reduce to sum and find maximum.

  • Recap Map, Reduce & Filter
  • Introduction to Numpy3:12

    Explore NumPy, the fundamental package for scientific computing in Python, offering a fast, memory-efficient multi-dimensional array and essential linear algebra capabilities for data analytics.

  • NumPy Basics10:27

    Master NumPy basics for scientific computing and data analysis. Create and inspect 1d and 2d arrays, learn axes and vectorized operations, and use initializations like zeros, ones, and random arrays.

  • Recap Numpy Basics
  • Operations on Numpy arrays3:17

    Explore numpy arrays: shape, dimensions, indexing, reshaping, stacking and splitting techniques. Perform element-wise operations with built-in math functions and custom user-defined functions.

  • Python Code Operations on Numpy4:13
  • Recap Operations on Numpy
  • Introduction to pandas3:02

    Learn pandas fundamentals, including series and data frames, creating data frames from dictionaries and files, and using groupby, aggregation, lambda-based apply, and pivot tables.

  • Python Code Pandas Series and Pandas Dataframes15:04
  • Python Code Pandas Selecting Columns Rows and Indexing10:06

    # Indexing and Selecting Data


    In this section, you will:


    * Select rows from a dataframe

    * Select columns from a dataframe

    * Select subsets of dataframes

  • Python Code Pandas Slicing and Dicing10:56

    Explore pandas slicing and dicing of sales data from CSV files in Python, learning to filter records by sales and profit thresholds, subset columns, and identify Bangalore customers.

  • Python Code Pandas and Merging and Concatenating21:35

    Learn to merge and concatenate data from multiple sources using pandas, aligning customer and product dimensions to derive marketing insights and ensure data accuracy.

  • Getting and cleaning data6:32

    Learn to fetch data from delimited files, relational databases, websites, and APIs; use Python libraries like BeautifulSoup and requests, and identify and handle missing values per column.

  • Vectors and Vector spaces5:03

    Learn vectors and vector spaces through linear algebra and multivariate calculus, mastering linear transformations, eigenvalues, and vector operations to visualize data for machine learning.

  • Linear Transformations And Matrices2:42

    Explore linear transformations and matrices, visualizing space distortion as squishing, stretching, and rotating. Understand vectors as rows and columns, and solve systems of linear equations for x1, x2, x3.

  • Eigen values and Eigen vectors3:44

    Explore eigenvalues and eigenvectors: learn how Ax = λx identifies directions preserved by transformations, how distinct eigenvalues ensure independence, and how matrices decompose as A = Q Λ Q−1.

  • Multivariable Calculus4:41
  • Introduction to data visualisation3:14

    Data visualization translates information into visual context to reveal patterns, trends, and outliers, aiding faster decisions and clearer communication in the data science process.

  • Basics of Visualisation3:51
  • Plotting data distributions4:13

    Visualize univariate and multivariate data distributions with plots, including two-dimensional scatter plots, to understand how data spreads across single and multiple attributes.

  • Plotting Categorical and Time-Series Data2:54
  • Conclusion3:06

Requirements

  • No programming experience is needed.

Description

Module-1​

Welcome to the Pre-Program Preparatory Content

Session-1:​

1) Introduction​

2) Preparatory Content Learning Experience

MODULE-2​

INTRODUCTION TO PYTHON

Session-1:​

Understanding Digital Disruption Course structure​

1) Introduction​

2) Understanding Primary Actions​

3) Understanding es & Important Pointers

Session-2:​

Introduction to python​

1) Getting Started — Installation​

2) Introduction to Jupyter Notebook​

The Basics Data Structures in Python

3) Lists​

4) Tuples​

5) Dictionaries​

6) Sets

Session-3:​

Control Structures and Functions​

1) Introduction​

2) If-Elif-Else​

3) Loops​

4) Comprehensions​

5) Functions​

6) Map, Filter, and Reduce​

7) Summary

Session-4:​

Practice Questions​

1) Practice Questions I​

2) Practice Questions II

Module-3​

Python for Data Science

Session-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) Summary

Session-2:​

Operations on NumPy Arrays​

1) Introduction​

2) Basic Operations​

3) Operations on Arrays​

4) Basic Linear Algebra Operations​

5) Summary

Session-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) Summary

Session-4:​

Getting and Cleaning Data​

1) Introduction

2) 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) Summary

Session-5:​

Practice Questions​

1) NumPy Practice Questions​

2) Pandas Practice Questions​

3) Pandas Practice Questions Solution

Module-4

Session-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) Summary

Session-2:​

Linear Transformations and Matrices​

1) Matrices: The Basics​

2) Matrix Operations - I​

3) Matrix Operations - II

4) Linear Transformations​

5) Determinants​

6) System of Linear Equations​

7) Inverse, Rank, Column and Null Space​

8) Least Squares Approximation​

9) Summary

Session-3:​

Eigenvalues and Eigenvectors​

1) Eigenvectors: What Are They?​

2) Calculating Eigenvalues and Eigenvectors​

3) Eigen decomposition of a Matrix​

4) Summary

Session-4:​

Multivariable Calculus

Module-5

Session-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 Visualisation

Session-2:​

Basics of Visualisation Introduction​

1) Data Visualisation Toolkit​

2) Components of a Plot​

3) Sub-Plots​

4) Functionalities of Plots​

5) Summary

Session-3:​

Plotting Data Distributions Introduction​

1) Univariate Distributions​

2) Univariate Distributions - Rug Plots​

3) Bivariate Distributions​

4) Bivariate Distributions - Plotting Pairwise Relationships​

5) Summary

Session-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) Summary

Session-5:​

1) Practice Questions I​

2) Practice Questions II

Who this course is for:

  • Beginner Python developers curious about Data Science and Machine Learning

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