Python for Data Science & Machine Learning Complete Course

This Learn Python for Data Science & Machine Learning from A-Z course enables you to delve deeply into the learn python for data science & machine learning from A-Z, to better explore, understand and apply relevant skills.

Through this Learn Python for Data Science & Machine Learning from A-Z course, you will gain world-class knowledge and understanding with a focus on learning python for data science & machine learning from A-Z, based on solving real-life issues.

This Learn Python for Data Science & Machine Learning from A-Zcourse includes a whole host of practical tips and advice, helping you to develop yourlearn python for data science & machine learning from A-Zskillsto become a data analyst, data engineer, web designer, researcher, data scientist,or related professionyou can be.

Packed with videos, PDFs and exercises, it'll equip you with what it takes to be successfulin today’s business landscape, covering a broad range of topics, including data science job roles, python for data science, statistics for data science, probability-hypothesis testing and much more.

Frontier Education provides those with no previous experience or working professionals with trailblazing, practice-based teaching, aimed to inspire and prepare you for a career-relevant industry.

• 1. Course Intro 1:11:59

  • 1: 1.1 Who is this Course for 02:44
  • 2: 1.2 DS + ML Marketplace 06:56
  • 3: 1.3 Data Science Job Opportunities 04:25
  • 4: 1.4 Data Science Job Roles 10:23
  • 5: 1.5 What is a Data Scientist 17:00
  • 6: 1.6 How To Get a Data Science Job 18:39
  • 7: 1.7 Data Science Projects Overview 11:52

• 2. DS+ML Concepts 1:06:38

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

• 3. Python For Data Science 3:53:21

  • 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

• 4. Statistics for Data Science 1:08:09

  • 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

• 5. Probability _ Hypothesis Testing 20:48

  • 42: 5.1 What Exactly 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

• 6. NumPy Data Analysis 52:33

  • 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

• 7. Pandas Data Analysis 33:58

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

• 8. Python Data Visualization 46:05

  • 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

• 9. Machine Learning Overview 26:03

  • 57: 9.1 Intro to ML 26:03

• 10. Data Loading _ Exploration 13:06

  • 58: 10.1 Exploratory Data Analysis 13:06

• 11. Data Cleaning 15:24

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

• 12. Feature Selecting and Engineering 06:11

  • 61: 12.1 Feature Engineering 06:11

• 13. Linear and Logistic Regression 49:19

  • 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

• 14. K Nearest Neighbors 1:36:48

  • 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 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

• 15. Decision Trees 2:51:10

  • 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 16:51
  • 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

• 16. Ensemble Learning _ Random Forests 1:44:06

  • 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

• 17. Support Vector Machines 1:41:56

  • 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

• 18. K Means 1:00:16

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

• 19. PCA 2:00:20

  • 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

• 20. Data Science Career 35:10

  • 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

In this Learn Python for Data Science & Machine Learning from A-Z course, you will learn and understand the core theories and practices in learning python for data science & machine learning from A-Z, developing a solid base of knowledge. You will understand core learning python for data science & machine learning from A-Z theories and practices and be able to think critically, actively contribute to the body of knowledge in the industry and push the boundaries with learning python for data science & machine learning from A-Z skills.

With expert guidance and a combination of videos, PDFs, and worksheets, this course is designed to prepare you for a career or learning journey.

Course curriculum:

• Section 1: Introduction to Python for Data Science & Machine Learning from A-Z

• Section 2: Data Science & Machine Learning Concepts

• Section 3: Python For Data Science

• Section 4: Statistics for Data Science

• Section 5: Probability and Hypothesis Testing

• Section 6: NumPy Data Analysis

• Section 7: Pandas Data Analysis

• Section 8: Python Data Visualisation

• Section 9: Introduction to Machine Learning

• Section 10: Data Loading & Exploration

• Section 11: Data Cleaning

• Section 12: Feature Selecting and Engineering

• Section 13: Linear and Logistic Regression

• Section 14: K Nearest Neighbours

• Section 15: Decision Trees

• Section 16: Ensemble Learning and Random Forests

• Section 17: Support Vector Machines

• Section 18: K-Means

• Section 19: PCA

• Section 20: Data Science Career

You’ll also be able to access several exclusive bonus resources to help you along with your Learn Python for Data Science & Machine Learning from A-Z journey, including:

• NumPy data analysis
• Pandas data analysis
• Ensemble learning _ random forests
• Support vector machines
• Data science career

Top reasons to Study Online at Frontier Education

• Tailor-made: Course adapted to market needs and interests

• Flexible programs: Study and work at your own pace on easy-to-use web platforms

• Online education: Progressive teaching methods with video or easy to understand the medium

• Multicultural: Connect with classmates from all corners of the globe

This Learn Python for Data Science & Machine Learning from A-Z is ideal for people looking to progress their career into a data analyst, for those who want to become data engineers, as well as looking to further develop their skills and knowledge.

No prior knowledge or experience required

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Reed courses certificate of completion

Digital certificate - Included