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2023 Numpy, Pandas And Matplotlib A-Z™: For Machine Learning
Last updated 1/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.30 GB | Duration: 11h 43m

Python NumPy, Pandas, and Matplotlib for Data Analysis, Data Science and Machine Learning. Pre-machine learning Analysis

What you'll learn

Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user

Dare to get the most out of Python NumPy, Pandas and Matplotlib

Go deeper to understand complex topics in Python NumPy, Pandas and data visualisation

Learn Python NumPy, Pandas and Matplotlib through several exercises and solutions

Acquire the required Python NumPy, Pandas and Matplotlib knowledge you need to excel in Data Science, Machine Learning, Ai and Deep Learning

Be trained by expert

Requirements

Just a little knowledge of Python

Description

Welcome to NumPy, Pandas and Matplotlib A-Z™: for Machine LearningNumPy 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 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.

Overview

Section 1: NumPy - Setups

Lecture 1 Course Syllabus Walkthrough

Lecture 2 Installing Jupiter Notebook

Lecture 3 Installing of NumPy

Lecture 4 Importing NumPy

Section 2: NumPy - Introduction

Lecture 5 What is NumPy

Lecture 6 What is Arrray

Lecture 7 Types of Array

Lecture 8 What is Dimension

Lecture 9 Exploring - Row Before Column - Why?

Lecture 10 Identifying an Array

Lecture 11 Scalar vs Vector vs Matrix vs Tensor

Section 3: NumPy - Creating Arrays

Lecture 12 First Time Creating an Array

Lecture 13 Creating an Array from a Tuple

Lecture 14 Creating a Zero Dimensional Array

Lecture 15 Avoiding Errors of "Multiple Arguments"

Lecture 16 Creating a 1-D Array

Lecture 17 Creating a 2-D Array

Lecture 18 Creating a 3-D Array

Section 4: NumPy - Data Type

Lecture 19 Understanding NumPy Data Type

Lecture 20 Forcing a Data Type of an Array

Section 5: NumPy - Challenges and Solution - Creating Arrays

Lecture 21 The Challenges

Lecture 22 The Challenges - text

Lecture 23 Solution to Challenge 1a

Lecture 24 Solution to Challenge 1b

Lecture 25 Solution to Challenge 1c

Lecture 26 Solution to Challenge 1d

Lecture 27 Solution to Challenge 1e

Lecture 28 Solution to Challenge 2a

Lecture 29 Solution to Challenge 2b

Lecture 30 Solution to Challenge 2c

Lecture 31 Solution to Challenge 2d

Lecture 32 Solution to Challenge 2e

Lecture 33 Solution to Challenge 2f

Section 6: NumPy - Creating Arrays - (Others)

Lecture 34 Array of Zeros

Lecture 35 Arrays of Ones

Lecture 36 Empty Arrays

Lecture 37 How to use arange()

Lecture 38 How to use linspace()

Lecture 39 How to use reshape()

Section 7: NumPy - Attributes of an Array

Lecture 40 How to find the attributes of an Array - (ndim, shape, size, dtype, itemsize)

Section 8: NumPy - Challenges and Solutions - Creating Arrays (More)

Lecture 41 The Challenges

Lecture 42 The Challenges - Text

Lecture 43 Solution to Challenge 1a

Lecture 44 Solution to Challenge 1b

Lecture 45 Solution to Challenge 1c

Lecture 46 Solution to Challenge 2a

Lecture 47 Solution to Challenge 2b

Lecture 48 Solution to Challenge 2c

Lecture 49 Solution to Challenge 2d

Lecture 50 Solution to Challenge 2e

Lecture 51 Solution to Challenge 2f

Lecture 52 Solution to Challenge #3

Lecture 53 Solution to Challenge #4

Section 9: NumPy - Array Sorting and Concatenation

Lecture 54 Array Sorting

Lecture 55 Array Concatenation

Section 10: NumPy - 1-D Array Indexing and Slicing

Lecture 56 Understanding how indexing and Slicing work on 1-D Arrays

Section 11: NumPy - Challenges and Solution - 1-D Array Indexing & Slicing

Lecture 57 The Challenges

Lecture 58 The Challenges - Text

Lecture 59 Solution to Challenge 1a

Lecture 60 Solution to Challenge 1b

Lecture 61 Solution to Challenge 1c

Lecture 62 Solution to Challenge 1d

Lecture 63 Solution to Challenge 1e

Lecture 64 Solution to Challenge 1f

Lecture 65 Solution to Challenge 1g

Lecture 66 Solution to Challenge 1h

Lecture 67 Solution to Challenge 1i

Lecture 68 Solution to Challenge 1j

Lecture 69 Solution to Challenge 1k

Lecture 70 Solution to Challenge 1l

Lecture 71 Solution to Challenge 1m

Section 12: NumPy - Creating an Array from Existing Array

Lecture 72 With Less Than, Greater Than or Equal To

Lecture 73 Even and Odd Numbers

Lecture 74 Two Conditions

Section 13: NumPy - Challenges and Solutions - Creating an Array from Existing Array

Lecture 75 The Challenges

Lecture 76 The Challenges - Text

Lecture 77 Solution to Challenge #1

Lecture 78 Solution to Challenge #2

Lecture 79 Solution to Challenge #3

Lecture 80 Solution to Challenge #4

Lecture 81 Solution to Challenge #5

Section 14: NumPy - 2-D Array Indexing and Slicing

Lecture 82 Selecting Elements of 2-D Array

Lecture 83 Slicing In 2-D Array

Section 15: NumPy - Challenges and Solution - 2-D Array Indexing & Slicing

Lecture 84 The Challenges

Lecture 85 The Challenges - Text

Lecture 86 Solution to Challenge #1

Lecture 87 Solution to Challenge #2

Lecture 88 Solution to Challenge #3

Lecture 89 Solution to Challenge #4

Lecture 90 Solution to Challenge #5

Lecture 91 Solution to Challenge #6

Lecture 92 Solution to Challenge #7

Section 16: NumPy - 3D Indexing and Slicing

Lecture 93 Selecting Elements of 3-D Array

Lecture 94 Slicing a 3-D Array

Lecture 95 More on Slicing

Section 17: NumPy - Challenges and Solution - 3-D Array Indexing & Slicing

Lecture 96 The Challenges

Lecture 97 The Challenges - Text

Lecture 98 Solution to Challenge #1

Lecture 99 Solution to Challenge #2

Lecture 100 Solution to Challenge #3

Lecture 101 Solution to Challenge #4

Lecture 102 Solution to Challenge #5

Lecture 103 Solution to Challenge #6

Lecture 104 Solution to Challenge #7

Lecture 105 Solution to Challenge #8

Lecture 106 Solution to Challenge #9

Lecture 107 Solution to Challenge #10

Lecture 108 Solution to Challenge #11

Lecture 109 Solution to Challenge #12

Lecture 110 Solution to Challenge #13

Lecture 111 Solution to Challenge #14

Lecture 112 Solution to Challenge #15

Lecture 113 Solution to Challenge #16

Lecture 114 Solution to Challenge #17

Section 18: NumPy - Summary - Selecting Element From Any n-D Array

Lecture 115 Summary on Selecting Element From any Dimensional Array

Section 19: NumPy - Array Flatten and Ravel

Lecture 116 Understanding Array Flatten and Ravel

Section 20: NumPy - Transpose

Lecture 117 Understanding Array Transpose

Section 21: NumPy - Reverse

Lecture 118 Understanding How to Reverse an Array

Lecture 119 Understanding How to Reverse Along an Axis

Section 22: NumPy - Unique Array

Lecture 120 Creating a Unique Array

Lecture 121 Indexing a Unique Array

Section 23: NumPy - Maximum, Minimum and Sum of an Array

Lecture 122 Minimum, Maximum & Sum

Lecture 123 Minimum, Maximum and Sum Along an Axis

Section 24: NumPy - Stacking

Lecture 124 Array Stacking

Section 25: NumPy - Splitting an Array

Lecture 125 Splitting an Array

Lecture 126 Splitting an Array on a Specific Column

Section 26: NumPy - Copying an Array

Lecture 127 Understand how to Copy an Array

Lecture 128 Understand how to Copy an Array II

Section 27: NumPy - Array Operators

Lecture 129 Understanding Array Operators

Section 28: NumPy - Deleting Elements

Lecture 130 How to delete Array Element I

Lecture 131 How to delete Array Element II

Lecture 132 Challenge & Solution I

Lecture 133 Challenge & Solution II

Lecture 134 Challenge & Solution III

Lecture 135 Challenge & Solution III - Code

Lecture 136 Challenge Yourself

Lecture 137 Solution - Challenge Yourself

Section 29: NumPy - Appending and Inserting Elements Into an Array

Lecture 138 How to append & Insert an Element Into An Array

Lecture 139 How to append & Insert Elements Into An Array

Section 30: NumPy - Newaxis

Lecture 140 Understanding Newaxis

Section 31: NumPy - Trigonometric Function

Lecture 141 Understanding NumPy Trigonometric Function

Lecture 142 Understanding NumPy Trigonometric Function

Section 32: NumPy - Searching Array

Lecture 143 Understanding How to Search an Array

Section 33: NumPy - Array Multiplication

Lecture 144 Array Multiplication by a Single Number

Lecture 145 Understanding dot()

Lecture 146 Challenge & Solution

Section 34: NumPy - Trace

Lecture 147 Understanding Trace

Lecture 148 Challenge & Solution

Section 35: NumPy - Outer Product

Lecture 149 Understanding Outer Product

Lecture 150 Challenge & Solution

Section 36: NumPy - Inner Product

Lecture 151 Understanding Inner Product

Section 37: NumPy - Cross Product

Lecture 152 Understanding Cross Product

Lecture 153 Challenge & Solution - I

Lecture 154 Challenge & Solution - II

Section 38: NumPy - Kronecker Product

Lecture 155 Understanding Kronecker Product

Section 39: NumPy - Determinant

Lecture 156 Understanding Determinant

Lecture 157 Challenge & Solution - 2 by 2

Lecture 158 Challenge & Solution - 3 by 3

Section 40: NumPy - Inverse of Array

Lecture 159 Understanding Inverse of Array

Lecture 160 Challenge & Solution

Section 41: NumPy - Condition Number

Lecture 161 Understanding the Condition Number

Section 42: NumPy - Random Sub-Module

Lecture 162 Random Number (Integer)

Lecture 163 Random Number (Float)

Lecture 164 Random Arrays

Lecture 165 Random Choice

Lecture 166 Choice with 2-D and 3-D Array

Section 43: NumPy - Seed

Lecture 167 Understanding Random Seed

Lecture 168 Random Seed With Choice()

Section 44: NumPy - Data Distribution

Lecture 169 What is Data Distribution?

Lecture 170 What is Random Distribution?

Lecture 171 Random Distribution 2-D and 3-D Array

Section 45: NumPy - Data Visualisation

Lecture 172 NumPy vs MatPlotLib vs Seaborn

Lecture 173 Installation of MatPlotLib and Seaborn

Lecture 174 Challenge & Solution 1

Lecture 175 Challenge & Solution II

Section 46: NumPy - Normal Distribution & Visualisation

Lecture 176 What is Normal Distribution

Lecture 177 Normal Distribution Visualisation

Section 47: NumPy - Binomial Distribution

Lecture 178 Binomial Distribution

Lecture 179 Binomial Data Visualisation

Section 48: Pandas - Intro, Installation & DataFrame

Lecture 180 Pandas Introduction

Lecture 181 Pandas Installation & Import

Lecture 182 Pandas DataFrame

Section 49: Resources Used for Pandas

Lecture 183 Happiness Data Set

Lecture 184 Sales Data Set

Lecture 185 Northwind Database

Lecture 186 Cities Data Set

Section 50: Pandas - Series

Lecture 187 Understanding Pandas Series

Section 51: Pandas - Label

Lecture 188 Understanding Pandas Label

Lecture 189 Creating Series From Dictionary

Section 52: Pandas - DataFrame

Lecture 190 Introduction to DataFrame in Pandas

Lecture 191 Loc

Lecture 192 Challenge & Solution

Section 53: Pandas - Concatenation

Lecture 193 Pandas - Understanding Concat in Pandas

Lecture 194 Pandas - Understanding Concat in Pandas - Code

Lecture 195 Pandas - Adding Hierarchy

Lecture 196 Pandas - Adding Hierarchy - Code

Lecture 197 Pandas - Concat Label

Lecture 198 Pandas - Concat Label - Code

Lecture 199 Pandas - Challenge & Solution

Lecture 200 Pandas - Challenge & Solution - Code

Lecture 201 Pandas - Concat Columns of Different Sizes

Lecture 202 Pandas - Concat Columns of Different Sizes - Code

Lecture 203 Pandas - Concat along axis

Lecture 204 Pandas - Concat along axis - Code

Section 54: Pandas - Merge

Lecture 205 Pandas - Understanding Merge

Lecture 206 Pandas - Understanding Merge - Code

Lecture 207 Pandas - Merging DataFrame of Different Sizes

Lecture 208 Pandas - Merging DataFrame of Different Sizes - Code

Lecture 209 Pandas - Inner, Outer, Left and Right Join

Lecture 210 Pandas - Inner, Outer, Left and Right Join - Code

Lecture 211 Pandas - Merge Suffix

Lecture 212 Pandas - Merge Suffix - Code

Section 55: Pandas - Load CSV

Lecture 213 Load CSV in Pandas

Section 56: Pandas - Aggregate & Statistics (Min, Max, Sum, Mean, Median, Mode, Summary)

Lecture 214 Pandas - Minimum and Maximum

Lecture 215 Pandas - Minimum and Maximum - Singapore

Lecture 216 Pandas - Mean, Median & Mode

Lecture 217 Pandas - Mean, Median & Mode - Mexico

Lecture 218 Pandas - Sum

Lecture 219 Challenge & Solution

Lecture 220 Pandas - Statistical Summary

Lecture 221 Pandas - Count

Section 57: Pandas - JSON

Lecture 222 Pandas - Load JSON

Section 58: Pandas - Challenges & Solutions

Lecture 223 1 - Pandas Challenge & Solution - Import

Lecture 224 2 - Pandas Challenge & Solution - Data Set Inspection - Shape, DataType & Column

Lecture 225 3 - Challenge & Solution - Skip Rows Reading CSV File

Lecture 226 3 - Challenge & Solution - Skip Rows Reading CSV File - Code

Lecture 227 4 - Challenge & Solution - Skip Rows Keep Headers

Lecture 228 4 - Challenge & Solution - Skip Rows Keep Headers - Code

Lecture 229 5 - Challenge & Solution - Read CSV Without Header

Lecture 230 5 - Challenge & Solution - Read CSV Without Header - Code

Lecture 231 6 - Challenge & Solution - Subset of Column

Lecture 232 6 - Challenge & Solution - Subset of Column - Code

Lecture 233 7 - Challenge & Solution - Few Rows

Lecture 234 7 - Challenge & Solution - Few Rows - Code

Lecture 235 8 - Challenge & Solution - Few Rows, Few Columns

Lecture 236 8 - Challenge & Solution - Few Rows, Few Columns - Code

Lecture 237 9 - Challenge & Solution - Time to Import

Lecture 238 9 - Challenge & Solution - Time to Import- Code

Lecture 239 10 - Challenge & Solution - Changing Data Type

Lecture 240 10 - Challenge & Solution - Changing Data Type - Code

Section 59: Pandas - Challenges & Solutions

Lecture 241 Pandas - Summary of Data Set

Lecture 242 Pandas - Summary of Data Set - Code

Lecture 243 Pandas - Subset of Column

Lecture 244 Pandas - Subset of Column - Code

Lecture 245 Pandas - Total number of Columns and Rows

Lecture 246 Pandas - Total number of Columns and Rows - Code

Lecture 247 Pandas - Last Ten Rows

Lecture 248 Pandas - Last Ten Rows - Code

Section 60: Pandas - Challenges & Solutions

Lecture 249 Pandas - Difference between Loc and iloc

Lecture 250 Pandas - Difference between Loc and iloc - more

Lecture 251 Pandas - Difference between head and tail

Lecture 252 Pandas - Difference between head and tail - Code

Lecture 253 Pan

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