Data Science and Machine Learning with Python Programming

Take your abilities to the next level with our industry-standard and profession-aligned Data Science and Machine Learning with Python Programming courses. This Data Science and Machine Learning with Python Programming course was created using sophisticated resources by our expert mentors. Enroll in this Data Science and Machine Learning with Python Programming course if you want to learn everything there is to know about Data Science and Machine Learning with Python Programming and improve your dream profession abilities.

Our instructor presents the techniques and frameworks that assist learners to accomplish the necessary subject matters in this Data Science and Machine Learning with Python Programming course. The complete Data Science and Machine Learning with Python Programming course is jam-packed with all of the required insights and examples from both the theoretical and practical elements of the linked subject; also, this Data Science and Machine Learning with Python Programming course is made for any creative learner who requires it.

You'll have access to well-known academics and industry figures, as well as a varied and professional cohort and all of the premium features, such as interactive classes, qualified and moderated examinations, podcasts, PDF books, and other high-engagement distance learning activities.

Furthermore, you can work with a diverse group of students from all around the world to tackle real-world challenges. This Data Science and Machine Learning with Python Programming course can help you improve your skills and put what you've learned to the test.

Why Pykinile?

• We provide a large selection of courses, including multi-language courses, diploma programmes, crash courses, professional development programmes, and non-credit-based academic courses, in addition to this Data Science and Machine Learning with Python Programming course.
• With a customisable and self-paced study schedule, this Data Science and Machine Learning with Python Programming course offers a whole unique learning experience.
• This Data Science and Machine Learning with Python Programming is a cost-effective and high-quality course with a flexible refund policy.
• Pykinile ensures that both former and current experts provide expert instruction. On our platform, you will receive the best education, tips, and tricks from industry experts.
• This Data Science and Machine Learning with Python Programming course provides an outstanding learning experience with access from any device at any time.
• You can learn Data Science and Machine Learning with Python Programming at your own pace; you determine how quickly you want to learn. We ensure comprehensive care and quality guidelines when it comes to professional courses.
• The Data Science and Machine Learning with Python Programming course was developed to meet UK and EU standards.

• Course Intro 1:11:56

  • 1: Who is this Course for Preview 02:43
  • 2: Data Science Projects Overview Preview 11:52
  • 3: DS + ML Marketplace 06:55
  • 4: Data Science Job Opportunities 04:24
  • 5: Data Science Job Roles 10:23
  • 6: What is a Data Scientist 17:00
  • 7: How To Get a Data Science Job 18:39

• DS+ML Concepts 03:14

  • 8: Why We Use Python 03:14

• Python For Data Science 1:48:41

  • 9: What is Python Programming 06:03
  • 10: Why Python for Data Science 04:35
  • 11: What is Jupyter 03:54
  • 12: What is Colab 03:27
  • 13: Jupyter Notebook 18:01
  • 14: More About Python Lists 15:08
  • 15: Python Tuples 11:25
  • 16: Compound Data Types and When to use each Data Type 12:58
  • 17: Functions 14:23
  • 18: Python Object Oriented Programming 18:47

• Statistics for Data Science 1:08:05

  • 19: Intro to Statistics 07:10
  • 20: Descriptive Statistics 06:35
  • 21: Measure of Variability 12:19
  • 22: Measure of Variability Continued 09:35
  • 23: Measures of Variable Relationship 07:37
  • 24: Inferential Statistics 15:18
  • 25: Measures of Asymmetry 01:57
  • 26: Sampling Distribution 07:34

• Probability _ Hypothesis Testing 20:46

  • 27: What Exactly Probability 03:44
  • 28: Expected Values 02:38
  • 29: Relative Frequency 05:15
  • 30: Hypothesis Testing Overview 09:09

• NumPy Data Analysis 52:30

  • 31: NumPy Array Data Types 12:58
  • 32: NumPy Arrays 08:21
  • 33: NumPy Array Basics 11:36
  • 34: NumPy Array Indexing 09:10
  • 35: NumPy Array Computations 05:53
  • 36: Broadcasting 04:32

• Pandas Data Analysis 33:57

  • 37: Intro to Pandas 15:52
  • 38: Intro to Panda Continued 18:05

• Python Data Visualization 46:04

  • 39: Data Visualization Overview 24:49
  • 40: Different Data Visualization Libraries in Python 12:48
  • 41: Python Data Visualization Implementation 08:27

• Machine Learning Overview 26:03

  • 42: Intro to ML 26:03

• Data Loading _ Exploration 13:05

  • 43: Exploratory Data Analysis 13:05

• Data Cleaning 15:23

  • 44: Feature Scaling 07:40
  • 45: Data Cleaning 07:43

• Feature Selecting and Engineering 06:11

  • 46: Feature Engineering 06:11

• Linear and Logistic Regression 49:16

  • 47: Linear Regression Intro 08:17
  • 48: Gradient Descent 05:58
  • 49: Linear Regression, Correlation Methods 26:33
  • 50: Linear Regression Implemenation 05:06
  • 51: Logistic Regression 03:22

• Decision Trees 2:29:39

  • 52: Decision Trees Section Overview 04:11
  • 53: EDA on Adult Dataset 16:53
  • 54: What is Entropy and Information Gain 21:50
  • 55: The Decision Tree ID3 algorithm from scratch Part 1 11:32
  • 56: The Decision Tree ID3 algorithm from scratch Part 2 07:35
  • 57: The Decision Tree ID3 algorithm from scratch Part 3 04:07
  • 58: Evaluating our ID3 implementation 16:51
  • 59: Compare with Sklearn implementation 08:51
  • 60: Visualizing the Tree 10:15
  • 61: Plot the features importance 05:51
  • 62: Decision Trees Hyper-parameters 11:39
  • 63: Pruning 17:11
  • 64: [Optional] Gain Ration 02:49
  • 65: Decision Trees Pros and Cons 07:31
  • 66: [Project] Predict whether income exceeds $50Kyr Overview 02:33

• Ensemble Learning Random Forests 1:11:44

  • 67: Ensemble Learning Section Overview 03:46
  • 68: What is Ensemble Learning 13:06
  • 69: What is Bootstrap Sampling 08:25
  • 70: Out of Bag Error 07:47
  • 71: Implementing Random Forests from scratch Part 2 06:10
  • 72: Compare with sklearn implementation 03:41
  • 73: Random Forests Pros and Cons 05:25
  • 74: What is Boosting 04:41
  • 75: AdaBoost Part 1 04:10
  • 76: AdaBoost Part 2 14:33

• K Means 1:00:15

  • 77: Unsupervised Machine Learning Intro 20:22
  • 78: Representation of Clusters 20:48
  • 79: Data Standardization 19:05

• PCA 1:33:12

  • 80: PCA Section Overview 05:12
  • 81: What is PCA 09:36
  • 82: PCA Drawbacks 03:31
  • 83: PCA Algorithm Steps 13:12
  • 84: PCA Cov vs SVD 04:58
  • 85: PCA Main Applications 02:50
  • 86: PCA Data Preprocessing Scratch 14:31
  • 87: PCA BiPlot 17:27
  • 88: PCA Feature Scaling and Screeplot 09:29
  • 89: PCA Supervised vs unsupervised 04:55
  • 90: PCA Visualization 07:31

With the help and knowledge of industry leaders, the innovative Data Science and Machine Learning with Python Programming was created. This Data Science and Machine Learning with Python Programming course has been meticulously designed to meet all of the learning criteria for making a significant contribution to the associated sector and beyond. Enrolling in this Data Science and Machine Learning with Python Programming course will give the student with vital knowledge and abilities for achieving their desired job and building a strong personal and professional reputation.

This online course was created to help motivated students become the best in their personal and professional sectors. Many students have completed and enjoyed this Data Science and Machine Learning with Python Programming course. This Data Science and Machine Learning with Python Programming course gave them the confidence they needed to pursue occupations that were both meaningful and rewarding. This one-of-a-kind Data Science and Machine Learning with Python Programming course is excellent for devoted and ambitious learners who want to excel at their career or profession.

The Data Science and Machine Learning with Python Programming is planned and developed in accordance with international standards, ensuring that all materials are authentic and respectable, hence enhancing valuable skills. Furthermore, this Data Science and Machine Learning with Python Programming course will enhance your CV and help you stand out from other possible applicants or business competitors. The Data Science and Machine Learning with Python Programming was created in such a way that learners can do it at any time and in any location. Most significantly, our final assessment, the certificate of completion, will certify your newly gained skills and knowledge, allowing you to enter a competitive job market.

After enrolling in this Data Science and Machine Learning with Python Programming course, you can use our tutor assistance to help you with any questions you may have, which you can send to our learner support staff via email. This top online course in Data Science and Machine Learning with Python Programming was created by specialists for the future-focused professional and will offer learners with the tools and frameworks they need to lead effectively in a fast changing environment. Take the Data Science and Machine Learning with Python Programming course right now to advance your skills.

Curriculum for the course: Data Science and Machine Learning with Python Programming

Here is a curriculum breakdown of the Data Science and Machine Learning with Python Programming course:

1 - 1. Course Intro

1 - Who is this Course for

2 - DS + ML Marketplace

3 - Data Science Job Opportunities

4 - Data Science Job Roles

5 - What is a Data Scientist

6 - How To Get a Data Science Job

7 - Data Science Projects Overview

2 - DS+ML Concepts

8 - Why We Use Python

3 - Python For Data Science

9 - What is Python Programming

10 - Why Python for Data Science

11 - What is Jupyter

12 - What is Colab

13 - Jupyter Notebook

14 - More About Python Lists

15 - Python Tuples

16 - Compound Data Types and When to use each Data Type

17 - Functions

18 - Python Object Oriented Programming

4 - 4. Statistics for Data Science

19 - Intro to Statistics

20 - Descriptive Statistics

21 - Measure of Variability

22 - Measure of Variability Continued

23 - Measures of Variable Relationship

24 - Inferential Statistics

25 - Measures of Asymmetry

26 - Sampling Distribution

5 - 5. Probability _ Hypothesis Testing

27 - What Exactly Probability

28 - Expected Values

29 - Relative Frequency

30 - Hypothesis Testing Overview

6 - 6. NumPy Data Analysis

31 - NumPy Array Data Types

32 - NumPy Arrays

33 - NumPy Array Basics

34 - NumPy Array Indexing

35 - NumPy Array Computations

36 - Broadcasting

7 - 7. Pandas Data Analysis

37 - Intro to Pandas

38 - Intro to Panda Continued

8 - 8. Python Data Visualization

39 - Data Visualization Overview

40 - Different Data Visualization Libraries in Python

41 - Python Data Visualization Implementation

9 - 9. Machine Learning Overview

42 - Intro to ML

10 - 10. Data Loading _ Exploration

43 - Exploratory Data Analysis

11 - 11. Data Cleaning

44 - Feature Scaling8

45 - Data Cleaning

12 - 12. Feature Selecting and Engineering

46 - Feature Engineering

13 - 13. Linear and Logistic Regression

47 - Linear Regression Intro

48 - Gradient Descent

49 - Linear Regression, Correlation Methods

50 - Linear Regression Implemenation

51 - Logistic Regression

14 - 15. Decision Trees

52 - Decision Trees Section Overview5

53 - EDA on Adult Dataset

54 - What is Entropy and Information Gain

55 - The Decision Tree ID3 algorithm from scratch Part 1

56 - The Decision Tree ID3 algorithm from scratch Part 2

57 - The Decision Tree ID3 algorithm from scratch Part 3

58 - Evaluating our ID3 implementation

59 - Compare with Sklearn implementation

60 - Visualizing the Tree

61 - Plot the features importance

62 - Decision Trees Hyper-parameters

63 - Pruning

64 - [Optional] Gain Ration

65 - Decision Trees Pros and Cons

66 - [Project] Predict whether income exceeds $50Kyr Overview

15 - 16. Ensemble Learning _ Random Forests

67 - Ensemble Learning Section Overview

68 - What is Ensemble Learning

69 - What is Bootstrap Sampling

70 - Out of Bag Error

71 - Implementing Random Forests from scratch Part 2

72 - Compare with sklearn implementation

73 - Random Forests Pros and Cons

74 - What is Boosting

75 - AdaBoost Part 1

76 - AdaBoost Part 2

16 - 18. K Means

77 - Unsupervised Machine Learning Intro

78 - Representation of Clusters

79 - Data Standardization

17 - 19. PCA

80 - PCA Section Overview

81 - What is PCA

82 - PCA Drawbacks

83 - PCA Algorithm Steps

84 - PCA Cov vs SVD

85 - PCA Main Applications

86 - PCA Data Preprocessing Scratch

87 - PCA BiPlot

88 - PCA Feature Scaling and Screeplot

89 - PCA Supervised vs unsupervised

90 - PCA Visualization

Certificate

You will receive a FREE instantly downloadable certificate for course completion once you have completed the Data Science and Machine Learning with Python Programming course. You will also be able to request a certificate from Pykinile as well. All of our certificates are available in PDF and print formats, with FREE Shipping in the United Kingdom.

The Data Science and Machine Learning with Python Programming training course is ideal for highly driven students who want to improve their personal and professional skills as well as prepare for the career of their dreams! This Data Science and Machine Learning with Python Programming course is also suitable for people who want to learn more about this subject and appreciate staying up to date on the newest news and information.

Enroll today in the Data Science and Machine Learning with Python Programming course and advance your professional abilities from the convenience of your own home!

To enrol in this course, there are no formal requirements. Any eager learner who meets the age requirements is welcome to join us.

• Anyone who is eager to study is eligible.
• Any smart device that has access to the internet, such as a smartphone, tablet, laptop, or desktop computer.

Studying the Data Science and Machine Learning with Python Programming course is designed to help you get the skills, knowledge, and the job of your dreams, or even if it is about your

desired promotion! Learn the essential skills and knowledge you need to exceed in your professional life with the help & guidance from our Data Science and Machine Learning with Python Programming course.

Currently there are no Q&As for this course. Be the first to ask a question.

Reed courses certificate of completion

Digital certificate - Included