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Data Science and Machine Learning with Python Language

Data Science and Machine Learning with Python Language


Academy of Skills

Summary

Price
£12 inc VAT
Study method
Online, On Demand What's this?
Duration
15.3 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
Additional info
  • Tutor is available to students

1 student purchased this course

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Overview

Do you wish to improve your Data Science and Machine Learning with Python Language current abilities? Do you want to take a professionally created, industry-relevant course that you can take at any time and from any location? Do you wish you could learn a new skill set in order to get that Data Science and Machine Learning with Python Language job? Continue reading!

Our teachers present the skills and frameworks that assist learners to overcome the relevant subject matters in this Data Science and Machine Learning with Python Language Course. The entire Data Science and Machine Learning with Python Language course is jam-packed with all of the necessary insights and examples from the theoretical and practical parts of the relevant subject; also, this Data Science and Machine Learning with Python Language course is created for any creative student who needs it.

Academy of Skills will provide all the resources and structure essential for students to pass all sections of this Data Science and Machine Learning with Python Language course. You'll get access to a varied group of well-known academics and industry professionals. Furthermore, you will collaborate with a diverse group of students from across the world to address real-world challenges. To ensure that you flourish in your job, we have packed the whole Data Science and Machine Learning with Python Language course with crucial insights and examples of both theoretical and practical elements of Data Science and Machine Learning with Python Language.

This premium online Data Science and Machine Learning with Python Language course ensures the growth of your professional skills while also providing international accreditation. All of the themes and subtopics in Data Science and Machine Learning with Python Languages are organised scientifically, taking into account the psychology of the learner and their total learning experience. The Data Science and Machine Learning with Python Language modules are all simple to comprehend, interactive, and bite-sized. You will be able to learn Data Science and Machine Learning with Python Language at your own speed, from any location, using any device that is appropriate. The Academy of Skills offers an internationally recognized certification for this Data Science and Machine Learning with Python Language course.

Certificate

Learners can request a FREE PDF Certificate of completion after successfully completing the Data Science and Machine Learning with Python Language course. An additional fee may be charged for Data Science and Machine Learning with Python Language Hardcopy Certificate and includes Free Shipping in the UK.

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  • AudioVisual Lesson
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    Get a customized study plan that matches your hectic schedule. Learn Data Science and Machine Learning with Python Language at your own speed while achieving your unique objectives.
  • Access to Top Instructors
    Learn Data Science and Machine Learning with Python Language from famous university and cultural institution graduates who will share their ideas and knowledge.
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    You get to choose when you wish to learn the Data Science and Machine Learning with Python Language. You are free to study whenever you choose.
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Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Curriculum

18
sections
97
lectures
15h 20m
total
    • 1: Who is this Course for 02:43
    • 2: DS + ML Marketplace 06:55
    • 3: Data Science Job Opportunities 04:24
    • 4: Data Science Job Roles 10:23
    • 5: What is a Data Scientist 17:00
    • 6: How To Get a Data Science Job 18:39
    • 7: Data Science Projects Overview 11:52
    • 8: Why We Use Python 03:14
    • 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
    • 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
    • 27: What Exactly Probability 03:44
    • 28: Expected Values 02:38
    • 29: Relative Frequency 05:15
    • 30: Hypothesis Testing Overview 09:09
    • 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
    • 37: Intro to Pandas 15:52
    • 38: Intro to Panda Continued 18:05
    • 39: Data Visualization Overview 24:49
    • 40: Different Data Visualization Libraries in Python 12:48
    • 41: Python Data Visualization Implementation 08:27
    • 42: Intro to ML 26:03
    • 43: Exploratory Data Analysis 13:05
    • 44: Feature Scaling 07:40
    • 45: Data Cleaning 07:43
    • 46: Feature Engineering 06:11
    • 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
    • 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
    • 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
    • 77: Unsupervised Machine Learning Intro 20:22
    • 78: Representation of Clusters 20:48
    • 79: Data Standardization 19:05
    • 80: Section Overview 05:12
    • 81: What is PCA 09:36
    • 82: Drawbacks 03:31
    • 83: Algorithm Steps 13:12
    • 84: Cov vs SVD 04:58
    • 85: Main Applications 02:50
    • 86: Data Preprocessing Scratch 14:31
    • 87: BiPlot 17:27
    • 88: Feature Scaling and Screeplot 09:29
    • 89: Supervised vs unsupervised 04:55
    • 90: Visualization 07:31
    • 91: Starting a Career in Data Science 02:54
    • 92: Data Science Resume 03:42
    • 93: Getting Started with Freelancing 04:44
    • 94: Top Freelancing Websites 05:18
    • 95: Personal Branding 05:27
    • 96: Importance of Website and Blog 03:42
    • 97: Networking dos and donts 03:50

Course media

Description

With the help and knowledge of industry specialists, this novel Data Science and Machine Learning with Python Language course has been put together. Data Science and Machine Learning with Python Language has been meticulously created to fulfill the learning needs that will enable you to make a significant contribution to the area and carve out a successful career path.

Data Science and Machine Learning with Python Language course was created to help motivated students become the best in their personal and professional lives. Many students have already completed and enjoyed this Data Science and Machine Learning with Python Language course. This Data Science and Machine Learning with Python Language education gave them the tools they needed to advance to more gratifying and rewarding jobs. This one-of-a-kind Data Science and Machine Learning with Python Language course is suitable for devoted and ambitious learners who want to be the best at their career or profession.

With the help and knowledge of industry leaders, the original Data Science and Machine Learning with Python Language was created. This Data Science and Machine Learning with Python Language has been meticulously created to suit all of the learning criteria for making a significant contribution to the associated subject and subsequent career path. By participating in this Data Science and Machine Learning with Python Language course, the student will receive valuable knowledge and skills that will help them land their ideal career and establish a strong personal and professional reputation.

After enrolling in this Data Science and Machine Learning with Python Language course, you may use our tutor's assistance to help you with any questions you may have, which you can send to our learner support staff through email. This Data Science and Machine Learning with Python Language is one of our most popular online courses, created by professionals for the future-focused professional and designed to provide learners with the tools and frameworks they need to lead successfully in a fast changing environment.

Enroll in the Data Science and Machine Learning with Python Language right now to advance your abilities.

Curriculum

Course Curriculum: Data Science and Machine Learning with Python Language

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

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 - 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 - Probability Hypothesis Testing

27 - What Exactly Probability

28 - Expected Values

29 - Relative Frequency

30 - Hypothesis Testing Overview

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 - Pandas Data Analysis

37 - Intro to Pandas

38 - Intro to Panda Continued

8 - Python Data Visualization

39 - Data Visualization Overview

40 - Different Data Visualization Libraries in Python

41 - Python Data Visualization Implementation

9 - Machine Learning Overview

42 - Intro to ML

10 - Data Loading Exploration

43 - Exploratory Data Analysis

11 - Data Cleaning

44 - Feature Scaling

45 - Data Cleaning

12 - Feature Selecting and Engineering

46 - Feature Engineering

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 - Decision Trees

52 - Decision Trees Section Overview

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 - 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 - K Means

77 - Unsupervised Machine Learning Intro

78 - Representation of Clusters

79 - Data Standardization

17 - PCA

80 - Section Overview

81 - What is PCA

82 - Drawbacks

83 - Algorithm Steps

84 - Cov vs SVD

85 - Main Applications

86 - Data Preprocessing Scratch

87 - BiPlot

88 - Feature Scaling and Screeplot

89 - Supervised vs unsupervised

90 - Visualization

18 - Starting A Career in Data Science

91 - Starting a Career in Data Science

92 - Data Science Resume

93 - Getting Started with Freelancing

94 - Top Freelancing Websites

95 - Personal Branding

96 - Importance of Website and Blog

97 - Networking dos and donts



Who is this course for?

The Data Science and Machine Learning with Python Language training course is ideal for highly driven students who wish to improve your personal and professional abilities while also preparing for the career of their dreams! This Data Science and Machine Learning with Python Language is also great for persons who want to learn more about this topic in depth and appreciate being up to speed on the newest facts and expertise.

Requirements

  • There are no official requirements for Data Science and Machine Learning with Python Language
  • Data Science and Machine Learning with Python Language requires a basic Internet connection
  • Data Science and Machine Learning with Python Language requires you to have access to a computer, tablet, or a mobile device
  • Knowledge of basic English



Career path

The Data Science and Machine Learning with Python Language course is meant to prepare you for the job of your dreams, a promotion at work, or being self-employed and starting your own business.

Courses from the Academy of Skills will help you enhance your profession and keep your skills current.



Questions and answers

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FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.