2023 Master Class On Data Science Using Python A-Z™ : For Ml
Date: 20 April 2023 | Posted by: mitsumi
2023 Master Class On Data Science Using Python A-Z™ : For Ml
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.43 GB | Duration: 5h 49m
Python NumPy, Pandas, Matplotlib and Seaborn for Data Analysis, Data Science and ML. Pre-machine learning Analysis.
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.43 GB | Duration: 5h 49m
Python NumPy, Pandas, Matplotlib and Seaborn for Data Analysis, Data Science and ML. Pre-machine learning Analysis.
What you'll learn
Students will learn how to create and manipulate arrays, perform mathematical operations on arrays, and use functions such as sorting, searching, and statistics
Students will learn how to create and manipulate Series and Data Frames.
Students will learn how to create plots and charts, customize the appearance of visualizations, and add annotations and labels.
NumPy, Pandas, and Matplotlib will typically teach students how to use these tools to analyze and visualize data.
Requirements
Little knowledge in Python will be an added advantage. Student can still learn python basics from the BONUS section.
Description
Welcome to 2023 Master class on Data Science using Python. NumPy is a leading scientific computing library in Python while Pandas is for data manipulation and analysis. Also, learn to use Matplotlib for data visualization. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy and Pandas are the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy and Pandas to the advanced NumPy and Pandas. This course will give you a solid understanding of NumPy, Pandas, and their functions.At the end of the course, you should be able to write complex arrays for real-life projects, manipulate and analyze real-world data using Pandas.WHO IS THIS COURSE FOR?√ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization.√ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib and Seaborn.√ This course is for you if you want to learn NumPy, Pandas, Matplotlib and Seaborn for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.√ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well.√ This course is for you if you are tired of NumPy, Pandas, Matplotlib and Seaborn courses that are too brief, too simple, or too complicated.√ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas.√ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd.√ This course is for you if plan to pass an interview soon.
Overview
Section 1: BONUS : Python Crash Course
Lecture 1 Variables in Python
Lecture 2 Conditionals & If statement
Lecture 3 Example for If statement
Lecture 4 If else statement
Lecture 5 Example of If else statement
Lecture 6 Nested If statement
Lecture 7 Example for Nested If statement
Lecture 8 Elif statement
Lecture 9 Example for Elif statement
Lecture 10 While loop
Lecture 11 Example of while loop
Lecture 12 For Loop
Lecture 13 Example of For Loop
Lecture 14 Break & Continue Statement
Lecture 15 Introduction to containers
Lecture 16 Creating and accessing lists in Python
Lecture 17 List indexing and slicing
Lecture 18 Working with List methods
Lecture 19 Working with operators on lists
Lecture 20 List Comprehension
Lecture 21 Tuple : definition
Lecture 22 Tuples
Lecture 23 Tuple Indexing & Slicing
Lecture 24 Manipulating Tuples
Lecture 25 Unpacking Tuples
Lecture 26 Sets
Lecture 27 Dictionaries
Lecture 28 Basics of dictionary
Lecture 29 Accessing dictionary
Lecture 30 len, str & type functions in dictionary
Lecture 31 Functions in python
Lecture 32 Example program1 on Functions
Lecture 33 Example program2 on functions
Section 2: Data Handling using Numpy
Lecture 34 Introduction to modules in python
Lecture 35 Creating & Displaying 1D array
Lecture 36 Understanding 1D array Index
Lecture 37 Creating Array of 0's and Array of 1's
Lecture 38 Sorting elements in 1D array
Lecture 39 Slicing a 1D array
Lecture 40 Mathematical Operations on Array
Lecture 41 Searching an element in a Array
Lecture 42 Filtering an array
Lecture 43 Checking whether given array is empty or not ?
Lecture 44 Creating & Displaying 2D array
Lecture 45 ndim Attribute
Lecture 46 Size Attribute
Lecture 47 Shape and reshape of array
Lecture 48 Creating an Identity Matrix
Lecture 49 arange()
Lecture 50 linspace()
Lecture 51 Random array
Lecture 52 Random matrix
Lecture 53 Creating a diagonal matrix
Lecture 54 Flatten a Matrix
Lecture 55 Computing Trace of a Matrix
Lecture 56 Finding Transpose of a Matrix
Lecture 57 Negative indexing to access elements in a 2D array
Section 3: Data Handling using Pandas
Lecture 58 Introduction to Pandas
Lecture 59 Working with series in Pandas
Lecture 60 Combining series with Numpy
Lecture 61 Finding number of elements in a series
Lecture 62 Computing mean, max and min in a series
Lecture 63 Sorting a Series
Lecture 64 Displaying Unique values in a Series
Lecture 65 Summary of series statistics
Section 4: Data Visualization using Matplotlib in Python
Lecture 66 Introduction to Matplotlib
Lecture 67 Creating Line Graph
Lecture 68 Creating Bar Graph
Lecture 69 Creating Scatter Graph
Lecture 70 Creating Histogram Graph
Lecture 71 Creating Pie Chart
Lecture 72 Creating 3D Plot
Lecture 73 Creating 3D Line graph
Section 5: Data Visualization using Seaborn in Python
Lecture 74 Understanding a sample Dataset (Downloadable)
Lecture 75 Introduction to Seaborn
Lecture 76 Swarm Plot
Lecture 77 Violin Plot
Lecture 78 Facet Grids
Lecture 79 Heatmap
Section 6: Problem Solving Assignments
Section 7: Projects
√ This course is for you if you want to learn NumPy, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning. √ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well. √ This course is for you if you are tired of NumPy, Pandas, and Matplotlib courses that are too brief, too simple, or too complicated. √ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib. √ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas. √ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization. √ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd. √ This course is for you if plan to pass an interview soon.
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