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Python for Data Science & Machine Learning from A-Z

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

Lunes Online Learning

Summary

Price
£150 inc VAT
Or £50.00/mo. for 3 months...
Study method
Online, On Demand What's this?
Duration
43.8 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
258 students purchased this course

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Overview

  • Learn how to become a professional Data Scientist, Data Engineer, Data Analyst or Consultant

  • Learn data cleaning, processing, wrangling and manipulation

  • How to create resume and land your first job as a Data Scientist

  • How to use Python for Data Science

  • How to write complex Python programs for practical industry scenarios

  • Learn Plotting in Python (graphs, charts, plots, histograms etc)

  • Learn to use NumPy for Numerical Data

  • Machine Learning and it's various practical applications

  • Supervised vs Unsupervised Machine Learning

  • Learn Regression, Classification, Clustering and Sci-kit learn

  • Machine Learning Concepts and Algorithms

  • K-Means Clustering

  • Use Python to clean, analyze, and visualize data

  • Building Custom Data Solutions

  • Statistics for Data Science

  • Probability and Hypothesis Testing

Curriculum

21
sections
160
lectures
43h 47m
total

    • 8: 2.1 Why We Use Python? Preview 03:15
    • 9: 2.2 What is Data Science? 13:24
    • 10: 2.3 What is Machine Learning? 14:22
    • 11: 2.4 Machine Learning Concepts & Algorithms 14:43
    • 12: 2.6 Machine Learning vs Deep Learning 11:10
    • 13: 2.7 What is Deep Learning? 09:44

    • 14: 3.1 What is Python Programming? 06:04
    • 15: 3.2 Why Python for Data Science? 04:36
    • 16: 3.3 What is Jupyter? 03:54
    • 17: 3.4 What is Colab? 03:28
    • 18: 3.5 Jupyter Notebook 18:01
    • 19: 3.6 Getting Started with Colab 09:08
    • 20: 3.7 Python Variables, Booleans and None 11:48
    • 21: 3.8 Python Operators 25:27
    • 22: 3.9 Python Numbers and Booleans 07:48
    • 23: 3.10 Python Strings 13:12
    • 24: 3.11 Python Conditional Statements 13:53
    • 25: 3.12 Python For Loops and While Loops 08:08
    • 26: 3.13 Python Lists 05:10
    • 27: 3.14 More About Python Lists 15:09
    • 28: 3.15 Python Tuples 11:25
    • 29: 3.16 Python Dictionaries 20:19
    • 30: 3.17 Python Sets 09:41
    • 31: 3.18 Compound Data Types and When to use each Data Type 12:58
    • 32: 3.19 Functions 14:24
    • 33: 3.20 Python Object Oriented Programming 18:48

    • 34: 4.1 Intro to Statistics 07:11
    • 35: 4.2 Descriptive Statistics 06:36
    • 36: 4.3 Measure of Variability 12:19
    • 37: 4.4 Measure of Variability Continued 09:35
    • 38: 4.5 Measures of Variable Relationship 07:37
    • 39: 4.6 Inferential Statistics 15:18
    • 40: 4.7 Measures of Asymmetry 01:58
    • 41: 4.8 Sampling Distribution 07:35

    • 42: 5.1 What Exactly is Probability? 03:45
    • 43: 5.2 Expected Values 02:38
    • 44: 5.3 Relative Frequency 05:16
    • 45: 5.4 Hypothesis Testing Overview 09:09

    • 46: 6.1 NumPy Array Data Types 12:59
    • 47: 6.2 NumPy Arrays 08:22
    • 48: 6.3 NumPy Array Basics 11:36
    • 49: 6.4 NumPy Array Indexing 09:10
    • 50: 6.5 NumPy Array Computations 05:53
    • 51: 6.6 Broadcasting 04:33

    • 52: 7.1 Intro to Pandas 15:53
    • 53: 7.2 Intro to Panda Continued 18:05

    • 54: 8.1 Data Visualization Overview 24:49
    • 55: 8.2 Different Data Visualization Libraries in Python 12:49
    • 56: 8.3 Python Data Visualization Implementation 08:27

    • 57: 9.1 Intro to Machine Learning 26:03

    • 58: 10.1 Exploratory Data Analysis 13:06

    • 59: 11.1 Feature Scaling 07:41
    • 60: 11.2 Data Cleaning 07:43

    • 61: 12.1 Feature Engineering 06:11

    • 62: 13.1 Linear Regression Intro 08:17
    • 63: 13.2 Gradient Descent 05:59
    • 64: 13.3 Linear Regression + Correlation Methods 26:33
    • 65: 13.4 Linear Regression Implemenation 05:07
    • 66: 13.5 Logistic Regression 03:23

    • 67: 14.1 KNN Overview 03:01
    • 68: 14.2 Parametic vs Non-Parametic Models 03:29
    • 69: 14.3 EDA on Iris Dataset 22:08
    • 70: 14.4 KNN - Intuition 02:17
    • 71: 14.5 Implement the KNN algorithm from scratch 11:45
    • 72: 14.6 Compare the Reuslt with Sklearn Library Preview 03:47
    • 73: 14.7 KNN Hyperparameter tuning using the cross-validation 10:47
    • 74: 14.8 The decision boundary visualization 04:56
    • 75: 14.9 KNN - Manhattan vs Euclidean Distance 11:21
    • 76: 14.10 KNN Scaling in KNN 06:01
    • 77: 14.11 Curse of dimensionality 08:10
    • 78: 14.12 KNN use cases 03:33
    • 79: 14.13 KNN pros and cons 05:33

    • 80: 15.1 Decision Trees Section Overview 04:12
    • 81: 15.2 EDA on Adult Dataset 16:54
    • 82: 15.3 What is Entropy and Information Gain 21:51
    • 83: 15.4 The Decision Tree ID3 algorithm from scratch Part 1 11:33
    • 84: 15.5 The Decision Tree ID3 algorithm from scratch Part 2 07:35
    • 85: 15.6 The Decision Tree ID3 algorithm from scratch Part 3 04:07
    • 86: 15.7 ID3 - Putting Everything Together 21:23
    • 87: 15.8 Evaluating our ID3 implementation 16:54
    • 88: 15.9 Compare with Sklearn implementation 08:52
    • 89: 15.10 Visualizing the Tree 10:15
    • 90: 15.11 Plot the features importance 05:52
    • 91: 15.12 Decision Trees Hyper-parameters 11:40
    • 92: 15.13 Pruning 17:11
    • 93: 15.14 [Optional] Gain Ration 02:49
    • 94: 15.15 Decision Trees Pros and Cons 07:32
    • 95: 15.16 [Project] Predict whether income exceeds $50Kyr - Overview 02:33

    • 96: 16.1 Ensemble Learning Section Overview 03:47
    • 97: 16.2 What is Ensemble Learning 13:06
    • 98: 16.3 What is Bootstrap Sampling 08:26
    • 99: 16.4 What is Bagging 05:20
    • 100: 16.5 Out-of-Bag Error 07:47
    • 101: 16.6 Implementing Random Forests from scratch Part 1 22:34
    • 102: 16.7 Implementing Random Forests from scratch Part 2 06:11
    • 103: 16.8 Compare with sklearn implementation 03:41
    • 104: 16.9 Random Forests Hyper-Parameters 04:23
    • 105: 16.10 Random Forests Pros and Cons 05:25
    • 106: 16.11 What is Boosting 04:42
    • 107: 16.12 AdaBoost Part 1 04:10
    • 108: 16.13 AdaBoost Part 2 14:34

    • 109: 17.1 SVM - Outline 05:16
    • 110: 17.2 SVM - SVM intuition 11:39
    • 111: 17.3 SVM - Hard vs Soft Margin 13:26
    • 112: 17.4 SVM - C HP 04:18
    • 113: 17.5 SVM - Kernel Trick 12:19
    • 114: 17.6 SVM - Kernel Types 18:14
    • 115: 17.7 SVM - Linear Dataset 13:35
    • 116: 17.8 SVM - Non-Linear Dataset 12:51
    • 117: 17.9 SVM with Regression 05:52
    • 118: 17.10 SVM - Project Overview 04:26

    • 119: 18.1 Unsupervised Machine Learning Intro 20:22
    • 120: 18.2 Representation of Clusters 20:49
    • 121: 18.3 Data Standardization 19:05

    • 122: 19.1 PCA - Section Overview 05:13
    • 123: 19.2 What is PCA 09:37
    • 124: 19.3 PCA - Drawbacks 03:32
    • 125: 19.4 PCA - Algorithm Steps 13:12
    • 126: 19.5 PCA - Cov vs SVD 04:58
    • 127: 19.6 PCA - Main Applications 02:50
    • 128: 19.7 PCA - Image Compression Scratch 27:01
    • 129: 19.8 PCA - Data Preprocessing Scratch 14:32
    • 130: 19.9 PCA - BiPlot 17:28
    • 131: 19.10 PCA - Feature Scaling and Screeplot 09:29
    • 132: 19.11 PCA - Supervised vs unsupervised 04:56
    • 133: 19.12 PCA - Visualization 07:32

    • 134: 20.1 Creating a Data Science Resume 06:45
    • 135: 20.2 Data Science Cover Letter 03:33
    • 136: 20.3 How To Contact Recruiters 04:20
    • 137: 20.4 Getting Started with Freelancing 04:13
    • 138: 20.5 Top Freelance Websites 05:35
    • 139: 20.6 Personal Branding 04:03
    • 140: 20.7 Networking Do_s and Don_ts 03:45
    • 141: 20.8 Importance of a Website 02:56

    • 142: Cold+Email+Template+_+Examples 02:00 PDF
    • 143: Cover+Letter+Template+_+Samples 06:00 PDF
    • 144: Data Science 600 Questions and Answers 3:19:00 PDF
    • 145: networking+guide 01:00 PDF
    • 146: pythondatasciencehandbook 8:20:00 PDF
    • 147: Python-for-Data-Analysis-2nd-Edition 8:07:00 PDF
    • 148: PythonForDataScience Cheatsheet 03:00 PDF
    • 149: Recruiter+Reach+Out+Template+and+Examples 01:00 PDF
    • 150: Resume+Checklist 01:00 PDF
    • 151: Importing Data 02:00 PDF
    • 152: Jupyter Notebook 03:00 PDF
    • 153: Matplotlib 03:00 PDF
    • 154: NumPy Basics 03:00 PDF
    • 155: Pandas 03:00 PDF
    • 156: Python Basics 02:00 PDF
    • 157: Scikit-Learn 02:00 PDF
    • 158: Seaborn 02:00 PDF
    • 159: Supervised Learning 08:00 PDF
    • 160: Unsupervised Learning 05:00 PDF

Course media

Description

Learn Python for Data Science & Machine Learning from A-Z

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.

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.

We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

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.

Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

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

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.

Who is this course for?

  • Students who want to learn about Python for Data Science & Machine Learning

Requirements

  • Students should have basic computer skills

  • Students would benefit from having prior Python Experience but not necessary

Questions and answers

No questions or answers found containing ''.

Khalid asked:

I have very basic computer skills. Will i be able to do this course. And is it beneficial for a person over the age of 60 years.?

Answer:

Hi Khalid, yes this course goes over the basic fundamentals of python and then python for data science. No matter the age, you'll be able to learn new skills.

This was helpful. Thank you for your feedback.
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Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Reviews

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Course rating
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Service
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Content
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Legal information

This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

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.

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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.
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