Machine Learning and Data Science with Python Masterclass

Learn how to use Machine Learning, Data Science, Matplotlib , NumPy, Plotly , Scikit-Learn , Tensorflow , Pandas, Seaborn , and more!

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Created by Online Courses Learn Online, On Your Schedule | Firste Last updated Mon, 07-Jun-2021 English
What will i learn?
  • • Use Python for Data Science and Machine Learning
  • • Use Spark for Big Data Analysis
  • • Implement Machine Learning Algorithms
  • • Learn to use NumPy for Numerical Data
  • • Learn to use Pandas for Data Analysis
  • • Learn to use Matplotlib for Python Plotting
  • • Learn to use Seaborn for statistical plots
  • • Use Plotly for interactive dynamic visualizations
  • • Use SciKit-Learn for Machine Learning Tasks
  • • K-Means Clustering
  • • Logistic Regression
  • • Linear Regression
  • • Random Forest and Decision Trees
  • • Natural Language Processing and Spam Filters
  • • Neural Networks
  • • Support Vector Machines

Curriculum for this course
141 Lessons 22:43:57 Hours
1 Introduction
7 Lessons 01:12:03 Hours
  • 1. Who is this course for 00:02:44
  • 2. Data science + machine learning marketplace 00:06:56
  • 3. Data science job opportunities 00:04:25
  • 4. Data science job roles 00:10:24
  • 5. What is data scientist 00:17:01
  • 6. How to get a data science job 00:18:40
  • 7. Data science projects overview 00:11:53
  • 8. Why we use python 00:03:15
  • 9. What is data science 00:13:24
  • 10. What is machine learning 00:14:23
  • 11. Machine learning concepts & algorithms 00:14:43
  • 12. What is deep learning 00:09:45
  • 13. Machine learning vs deep learning 00:11:10
  • 14. What is programming 00:06:04
  • 15. Why python for data concepts 00:04:36
  • 16. What is jupyter 00:03:55
  • 17. What is google colab 00:03:28
  • 18. Python variables, Booleans and none 00:11:48
  • 19. Getting started with google colab 00:09:08
  • 20. Python operators 00:25:27
  • 21. Python numbers & Booleans 00:07:48
  • 22. Python strings 00:13:12
  • 23. Python conditional statements 00:13:54
  • 24. Python for loops and while loops 00:08:08
  • 25. Python lists 00:05:11
  • 26. More about lists 00:15:09
  • 27. Python tuples 00:11:26
  • 28. Python dictionaries 00:20:20
  • 29. Python sets 00:09:42
  • 30. Compound datatypes & when to use each one 00:12:59
  • 31. Python functions 00:14:24
  • 32. Object oriented programming in python 00:18:48
  • 33. Intro to statistics 00:07:11
  • 34. Descriptive statistics 00:06:36
  • 35. Measure of variability 00:12:20
  • 36. Measure of variability continued 00:09:36
  • 37. Measure of variability relationship 00:07:38
  • 38. Inferential statistics 00:15:18
  • 39. Measure of asymmetry 00:01:58
  • 40. Sampling distribution 00:07:35
  • 41. What exactly is probability 00:03:45
  • 42. Expected values 00:02:39
  • 43. Relative frequency 00:05:16
  • 44. Hypothesis testing overview 00:09:10
  • 45. Intro numpy array datatypes ( 1.1 NumPy Basics PDF ) 00:12:59
  • 46. Numpy arrays 00:08:22
  • 47. Numpy arrays basics 00:11:37
  • 48. Numpy array Indexing 00:09:11
  • 49. Numpy array computations 00:05:54
  • 50. Broadcasting 00:04:33
  • 51. Introduction to pandas (1.1 Pandas & 1.2 Pandas Basics PDF ) 00:15:53
  • 52. Introduction to pandas continues 00:18:06
  • 53. Data Visualization Overview 00:24:50
  • 54. Different Data Visualization Libraries in Python 00:06:11
  • 55. Python Data Visualization Implementation 00:08:28
  • 56. Introduction To Machine Learning (1.1 Supervised Learning PDF ) 00:26:04
  • 57. Exploratory Data Analysis 00:13:06
  • 58. Feature scaling 00:07:41
  • 59. Data cleaning 00:07:44
  • 60. Feature Engineering 00:06:12
  • 61. Linear Regression Intro 00:08:18
  • 62. Gradient Descent 00:05:59
  • 63. Linear Regression + Correlation Methods 00:26:34
  • 64. Linear Regression Implementation 00:05:07
  • 65. Logistic Regression 00:03:23
  • 66. KNN Overview 00:03:02
  • 67. Parametric vs non-parametric models 00:03:29
  • 68. EDA on Iris Dataset 00:22:09
  • 69. The KNN Intuition 00:02:17
  • 70. Implement the KNN algorithm from scratch 00:11:46
  • 71. Compare the result with the sklearn library 00:03:48
  • 72. Hyperparameter tuning using the cross-validation 00:10:48
  • 73. The decision boundary visualization 00:04:56
  • 74. Manhattan vs Euclidean Distance 00:05:33
  • 75. Feature scaling in KNN 00:06:02
  • 76. Curse of dimensionality 00:08:10
  • 77. KNN use cases 00:03:33
  • 78. KNN pros and cons 00:05:33
  • 79. Decision Trees Section Overview 00:04:12
  • 80. EDA on Adult Dataset 00:16:54
  • 81. What is Entropy and Information Gain 00:21:51
  • 82. The Decision Tree ID3 algorithm from scratch Part 1 00:11:33
  • 83. The Decision Tree ID3 algorithm from scratch Part 2 00:07:36
  • 84. The Decision Tree ID3 algorithm from scratch Part 3 00:04:08
  • 85. ID3 - Putting Everything Together 00:21:24
  • 86. Evaluating our ID3 implementation 00:16:52
  • 87. Compare with Sklearn implementation 00:08:52
  • 88. Visualizing the tree 00:10:16
  • 89. Plot the features importance 00:05:52
  • 90. Decision Trees Hyper-parameters 00:11:40
  • 91. Pruning 00:17:12
  • 92. [Optional] Gain Ration 00:02:50
  • 93. Decision Trees Pros and Cons 00:07:32
  • 94. [Project] Predict whether income exceeds $50Kyr – Overview 00:02:34
  • 95. Ensemble Learning Section Overview 00:03:47
  • 96. What is Ensemble Learning 00:13:06
  • 97. What is Bootstrap Sampling 00:08:26
  • 98. What is Bagging 00:05:21
  • 99. Out-of-Bag Error (OOB Error) 00:07:48
  • 100. Implementing Random Forests from scratch Part 1 00:22:35
  • 101. Implementing Random Forests from scratch Part 2 00:06:11
  • 102. Compare with sklearn implementation 00:03:42
  • 103. Random Forests Hyper-Parameters 00:04:24
  • 104. Random Forests Pros and Cons 00:05:26
  • 105. What is Boosting 00:04:42
  • 106. AdaBoost Part 1 00:04:11
  • 107. AdaBoost Part 2 00:14:34
  • 108. SVM Outline 00:05:16
  • 109. SVM intuition 00:11:39
  • 110. Hard vs Soft Margins 00:13:26
  • 111. C hyper-parameter 00:04:18
  • 112. Kernel Trick 00:12:19
  • 113. SVM - Kernel Types 00:18:14
  • 114. SVM with Linear Dataset (Iris) 00:13:36
  • 115. SVM with Non-linear Dataset 00:12:51
  • 116. SVM with Regression 00:05:52
  • 117. SMV - Project Overview 00:04:27
  • 118. Unsupervised Machine Learning Intro 00:20:23
  • 119. Unsupervised Machine Learning Continued 00:20:49
  • 120. Representing Clusters 00:19:06
  • 121. PCA Section Overview 00:05:13
  • 122. What is PCA 00:09:37
  • 123. PCA Drawbacks 00:03:32
  • 124. PCA Algorithm Steps (Mathematics) 00:13:13
  • 125. Covariance Matrix vs SVD 00:04:59
  • 126. PCA - Main Applications 00:02:51
  • 127. PCA - Image Compression 00:27:01
  • 128. PCA - Data Preprocessing 00:14:32
  • 129. PCA - Biplot and the Screen Plot 00:17:28
  • 130. PCA - Feature Scaling and Screen Plot 00:09:30
  • 131. PCA - Supervised vs Unsupervised 00:04:56
  • 132. PCA – Visualization 00:07:32
  • 133. Creating A Data Science Resume 00:06:46
  • 134. Data Science Cover Letter 00:03:34
  • 135. How to Contact Recruiters 00:04:21
  • 136. Getting Started with Freelancing 00:04:14
  • 137. Top Freelance Websites 00:05:35
  • 138. Personal Branding 00:04:03
  • 139. Networking Do's and Don'ts 00:03:46
  • 140. Importance of a Website 00:02:57
  • 141. Bonus Lectures. Enjoy the Benefits
Requirements
  • All Levels. No prior knowledge is required.
  • You will need a computer, Laptop, Tablet or a smartphone.
  • All devices work: Mac or PC, iOS or Android
  • All you need is YOU and YOU! Just have an open mind and willingness to learn and implement
+ View more
Description

Machine Learning and Data Science with Python Masterclass 2021

Learn how to use Machine Learning, Data Science, Matplotlib , NumPy, Plotly , Scikit-Learn , Tensorflow , Pandas, Seaborn , and more!

This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +

NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

The course covers 5 main areas:

1: PYTHON FOR DS+ML COURSE INTRO

This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning with Python

Data Science Industry and Marketplace

Data Science Job Opportunities

How To Get a Data Science Job

Machine Learning Concepts & Algorithms

2: PYTHON DATA ANALYSIS/VISUALIZATION

This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

Python Crash Course

NumPy Data Analysis

Pandas Data Analysis

Matplotlib

Seaborn

Plotly

3: MATHEMATICS FOR DATA SCIENCE

This section gives you a full introduction to the mathematics for data science such as statistics and probability.

Descriptive Statistics

Measure of Variability

Inferential Statistics

Probability

Hypothesis Testing

4: MACHINE LEARNING

This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

Intro to Machine Learning

Data Preprocessing

Linear Regression

Logistic Regression

K-Nearest Neighbors

Decision Trees

Ensemble Learning

Support Vector Machines

K-Means Clustering

PCA

5: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume

Creating a Cover Letter

Personal Branding

Freelancing + Freelance websites

Importance of Having a Website

Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

 

Here is some of What you'll learn:

<!--[if !supportLists]-->·       <!--[endif]-->140+ Python Machine Learning and Data Science videos

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

 

Thank you very much for getting started with Master Python for Machine Learning and Data Science.”

We imagine you are going to love what’s next. Go Hack’m

Don’t waste your time

REMEMBER… You will get lifetime access to over 140 lectures.

So, what are you waiting for? Click the buy button NOW, increase your knowledge, become a Professional Machine Learning Expert and advance your career all in a fun and practical way!

Don’t miss this Limited Time Offer. ACT NOW!

 

You will Learn by Practice:

By the end of this Unique Course, you will go from #Newbie to #Advanced as an #Python_Programmer. Here is what you’ll learn:

1 Introduction

<!--[if !supportLists]-->1.       <!--[endif]-->Who is this course for

<!--[if !supportLists]-->2.       <!--[endif]-->Data science + machine learning marketplace

<!--[if !supportLists]-->3.       <!--[endif]-->Data science job opportunities

<!--[if !supportLists]-->4.       <!--[endif]-->Data science job roles

<!--[if !supportLists]-->5.       <!--[endif]-->What is data scientist

<!--[if !supportLists]-->6.       <!--[endif]-->How to get a data science job

<!--[if !supportLists]-->7.       <!--[endif]-->Data science projects overview

2 Data Science and Machine Learning Concepts

<!--[if !supportLists]-->8.       <!--[endif]-->Why we use python

<!--[if !supportLists]-->9.       <!--[endif]-->What is data science

<!--[if !supportLists]-->10.   <!--[endif]-->What is machine learning

<!--[if !supportLists]-->11.   <!--[endif]-->Machine learning concepts & algorithms

<!--[if !supportLists]-->12.   <!--[endif]-->What is deep learning

<!--[if !supportLists]-->13.   <!--[endif]-->Machine learning vs deep learning

3 Python For Data Science

<!--[if !supportLists]-->14.   <!--[endif]-->What is programming

<!--[if !supportLists]-->15.   <!--[endif]-->Why python for data concepts

<!--[if !supportLists]-->16.   <!--[endif]-->What is jupyter

<!--[if !supportLists]-->17.   <!--[endif]-->What is google colab

<!--[if !supportLists]-->18.   <!--[endif]-->Python variables, Booleans and none

<!--[if !supportLists]-->19.   <!--[endif]-->Getting started with google colab

<!--[if !supportLists]-->20.   <!--[endif]-->Python operators

<!--[if !supportLists]-->21.   <!--[endif]-->Python numbers & Booleans

<!--[if !supportLists]-->22.   <!--[endif]-->Python strings

<!--[if !supportLists]-->23.   <!--[endif]-->Python conditional statements

<!--[if !supportLists]-->24.   <!--[endif]-->Python for loops and while loops

<!--[if !supportLists]-->25.   <!--[endif]-->Python lists

<!--[if !supportLists]-->26.   <!--[endif]-->More about lists

<!--[if !supportLists]-->27.   <!--[endif]-->Python tuples

<!--[if !supportLists]-->28.   <!--[endif]-->Python dictionaries

<!--[if !supportLists]-->29.   <!--[endif]-->Python sets

<!--[if !supportLists]-->30.   <!--[endif]-->Compound datatypes & when to use each one

<!--[if !supportLists]-->31.   <!--[endif]-->Python functions

<!--[if !supportLists]-->32.   <!--[endif]-->Object oriented programming in python

4 Statistics for Data Science

<!--[if !supportLists]-->33.   <!--[endif]-->Intro to statistics

<!--[if !supportLists]-->34.   <!--[endif]-->Descriptive statistics

<!--[if !supportLists]-->35.   <!--[endif]-->Measure of variability

<!--[if !supportLists]-->36.   <!--[endif]-->Measure of variability continued

<!--[if !supportLists]-->37.   <!--[endif]-->Measure of variability relationship

<!--[if !supportLists]-->38.   <!--[endif]-->Inferential statistics

<!--[if !supportLists]-->39.   <!--[endif]-->Measure of asymmetry

<!--[if !supportLists]-->40.   <!--[endif]-->Sampling distribution

5 Probability & Hypothesis Testing

<!--[if !supportLists]-->41.   <!--[endif]-->What is exactly is probability

<!--[if !supportLists]-->42.   <!--[endif]-->Expected values

<!--[if !supportLists]-->43.   <!--[endif]-->Relative frequency

<!--[if !supportLists]-->44.   <!--[endif]-->Hypothesis testing overview

6 NumPy Data Analysis

<!--[if !supportLists]-->45.   <!--[endif]-->Intro numpy array datatypes ( 1.1 NumPy Basics  PDF )

<!--[if !supportLists]-->46.   <!--[endif]-->Numpy arrays

<!--[if !supportLists]-->47.   <!--[endif]-->Numpy arrays basics

<!--[if !supportLists]-->48.   <!--[endif]-->Numpy array Indexing

<!--[if !supportLists]-->49.   <!--[endif]-->Numpy array computations

<!--[if !supportLists]-->50.   <!--[endif]-->Broadcasting

7 Pandas Data Analysis

<!--[if !supportLists]-->51.   <!--[endif]-->Introduction to pandas (1.1 Pandas & 1.2 Pandas Basics PDF )

<!--[if !supportLists]-->52.   <!--[endif]-->Introduction to pandas continues

8 Python Data Visualization

<!--[if !supportLists]-->53.   <!--[endif]-->Data Visualization Overview

<!--[if !supportLists]-->54.   <!--[endif]-->Different Data Visualization Libraries in Python

<!--[if !supportLists]-->55.   <!--[endif]-->Python Data Visualization Implementation

9 Machine Learning

<!--[if !supportLists]-->56.   <!--[endif]-->Introduction To Machine Learning (1.1 Supervised Learning PDF )

10 Data Loading & Exploration

<!--[if !supportLists]-->57.   <!--[endif]-->Exploratory Data Analysis

11 Data Cleaning

<!--[if !supportLists]-->58.   <!--[endif]-->Feature scaling

<!--[if !supportLists]-->59.   <!--[endif]-->Data cleaning

12 Feature Selecting and Engineering

<!--[if !supportLists]-->60.   <!--[endif]-->Feature Engineering

13 Linear and Logistic Regression

<!--[if !supportLists]-->61.   <!--[endif]-->Linear Regression Intro

<!--[if !supportLists]-->62.   <!--[endif]-->Gradient Descent

<!--[if !supportLists]-->63.   <!--[endif]-->Linear Regression + Correlation Methods

<!--[if !supportLists]-->64.   <!--[endif]-->Linear Regression Implementation

<!--[if !supportLists]-->65.   <!--[endif]-->Logistic Regression

14 K Nearest Neighbors

<!--[if !supportLists]-->66.   <!--[endif]-->KNN Overview

<!--[if !supportLists]-->67.   <!--[endif]-->Parametric vs non-parametric models

<!--[if !supportLists]-->68.   <!--[endif]-->EDA on Iris Dataset

<!--[if !supportLists]-->69.   <!--[endif]-->The KNN Intuition

<!--[if !supportLists]-->70.   <!--[endif]-->Implement the KNN algorithm from scratch

<!--[if !supportLists]-->71.   <!--[endif]-->Compare the result with the sklearn library

<!--[if !supportLists]-->72.   <!--[endif]-->Hyperparameter tuning using the cross-validation

<!--[if !supportLists]-->73.   <!--[endif]-->The decision boundary visualization

<!--[if !supportLists]-->74.   <!--[endif]-->Manhattan vs Euclidean Distance

<!--[if !supportLists]-->75.   <!--[endif]-->Feature scaling in KNN

<!--[if !supportLists]-->76.   <!--[endif]-->Curse of dimensionality

<!--[if !supportLists]-->77.   <!--[endif]-->KNN use cases

<!--[if !supportLists]-->78.   <!--[endif]-->KNN pros and cons

15 Decision Trees

<!--[if !supportLists]-->79.   <!--[endif]-->Decision Trees Section Overview

<!--[if !supportLists]-->80.   <!--[endif]-->EDA on Adult Dataset

<!--[if !supportLists]-->81.   <!--[endif]-->What is Entropy and Information Gain

<!--[if !supportLists]-->82.   <!--[endif]-->The Decision Tree ID3 algorithm from scratch Part 1

<!--[if !supportLists]-->83.   <!--[endif]-->The Decision Tree ID3 algorithm from scratch Part 2

<!--[if !supportLists]-->84.   <!--[endif]-->The Decision Tree ID3 algorithm from scratch Part 3

<!--[if !supportLists]-->85.   <!--[endif]-->ID3 - Putting Everything Together

<!--[if !supportLists]-->86.   <!--[endif]-->Evaluating our ID3 implementation

<!--[if !supportLists]-->87.   <!--[endif]-->Compare with Sklearn implementation

<!--[if !supportLists]-->88.   <!--[endif]-->Visualizing the tree

<!--[if !supportLists]-->89.   <!--[endif]-->Plot the features importance

<!--[if !supportLists]-->90.   <!--[endif]-->Decision Trees Hyper-parameters

<!--[if !supportLists]-->91.   <!--[endif]-->Pruning

<!--[if !supportLists]-->92.   <!--[endif]-->[Optional] Gain Ration

<!--[if !supportLists]-->93.   <!--[endif]-->Decision Trees Pros and Cons

<!--[if !supportLists]-->94.   <!--[endif]-->[Project] Predict whether income exceeds $50Kyr – Overview

16 Ensemble Learning and Random Forests

<!--[if !supportLists]-->95.   <!--[endif]-->Ensemble Learning Section Overview

<!--[if !supportLists]-->96.   <!--[endif]-->What is Ensemble Learning

<!--[if !supportLists]-->97.   <!--[endif]-->What is Bootstrap Sampling

<!--[if !supportLists]-->98.   <!--[endif]-->What is Bagging

<!--[if !supportLists]-->99.   <!--[endif]-->Out-of-Bag Error (OOB Error)

<!--[if !supportLists]-->100.                       <!--[endif]-->Implementing Random Forests from scratch Part 1

<!--[if !supportLists]-->101.                       <!--[endif]-->Implementing Random Forests from scratch Part 2

<!--[if !supportLists]-->102.                       <!--[endif]-->Compare with sklearn implementation

<!--[if !supportLists]-->103.                       <!--[endif]-->Random Forests Hyper-Parameters

<!--[if !supportLists]-->104.                       <!--[endif]-->Random Forests Pros and Cons

<!--[if !supportLists]-->105.                       <!--[endif]-->What is Boosting

<!--[if !supportLists]-->106.                       <!--[endif]-->AdaBoost Part 1

<!--[if !supportLists]-->107.                       <!--[endif]-->AdaBoost Part 2

17 Support Vector Machines

<!--[if !supportLists]-->108.                       <!--[endif]-->SVM Outline

<!--[if !supportLists]-->109.                       <!--[endif]-->SVM intuition

<!--[if !supportLists]-->110.                       <!--[endif]-->Hard vs Soft Margins

<!--[if !supportLists]-->111.                       <!--[endif]-->C hyper-parameter

<!--[if !supportLists]-->112.                       <!--[endif]-->Kernel Trick

<!--[if !supportLists]-->113.                       <!--[endif]-->SVM - Kernel Types

<!--[if !supportLists]-->114.                       <!--[endif]-->SVM with Linear Dataset (Iris)

<!--[if !supportLists]-->115.                       <!--[endif]-->SVM with Non-linear Dataset

<!--[if !supportLists]-->116.                       <!--[endif]-->SVM with Regression

<!--[if !supportLists]-->117.                       <!--[endif]-->SMV - Project Overview

18 K-means

<!--[if !supportLists]-->118.                       <!--[endif]-->Unsupervised Machine Learning Intro

<!--[if !supportLists]-->119.                       <!--[endif]-->Unsupervised Machine Learning Continued

<!--[if !supportLists]-->120.                       <!--[endif]-->Representing Clusters

19 PCA

<!--[if !supportLists]-->121.                       <!--[endif]-->PCA Section Overview

<!--[if !supportLists]-->122.                       <!--[endif]-->What is PCA

<!--[if !supportLists]-->123.                       <!--[endif]-->PCA Drawbacks

<!--[if !supportLists]-->124.                       <!--[endif]-->PCA Algorithm Steps (Mathematics)

<!--[if !supportLists]-->125.                       <!--[endif]-->Covariance Matrix vs SVD

<!--[if !supportLists]-->126.                       <!--[endif]-->PCA - Main Applications

<!--[if !supportLists]-->127.                       <!--[endif]-->PCA - Image Compression

<!--[if !supportLists]-->128.                       <!--[endif]-->PCA - Data Preprocessing

<!--[if !supportLists]-->129.                       <!--[endif]-->PCA - Biplot and the Screen Plot

<!--[if !supportLists]-->130.                       <!--[endif]-->PCA - Feature Scaling and Screen Plot

<!--[if !supportLists]-->131.                       <!--[endif]-->PCA - Supervised vs Unsupervised

<!--[if !supportLists]-->132.                       <!--[endif]-->PCA – Visualization

20 Data Science Career

<!--[if !supportLists]-->133.                       <!--[endif]-->Creating A Data Science Resume

<!--[if !supportLists]-->134.                       <!--[endif]-->Data Science Cover Letter

<!--[if !supportLists]-->135.                       <!--[endif]-->How to Contact Recruiters

<!--[if !supportLists]-->136.                       <!--[endif]-->Getting Started with Freelancing

<!--[if !supportLists]-->137.                       <!--[endif]-->Top Freelance Websites

<!--[if !supportLists]-->138.                       <!--[endif]-->Personal Branding

<!--[if !supportLists]-->139.                       <!--[endif]-->Networking Do's and Don'ts

<!--[if !supportLists]-->140.                       <!--[endif]-->Importance of a Website

21 Additional Content: Grand Finale

<!--[if !supportLists]-->141.                       <!--[endif]-->Bonus Lectures. Enjoy the Benefits

 

You could also end up using these skills in your work for Your #Clients, and much more.

 

We really hope you find this course valuable, but either way, please leave a review and share your experience...

 

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