Python for Data Science and Machine Learning from A-Z

This Python for Data Science and Machine Learning from A-Z Course is designed to provide you with knowledge to ensure a high standard of learning about Machine Learning. The course is crafted specially for distance learning. We divided our courses into smaller easily digestible modules, so that you can maintain attention throughout the course. SkillArts is duty bound to provide you with top level elearning, produced and maintained by industry experts.

Learning Outcome of this Python for Data Science and Machine Learning Course:

• Acquire the skill to clean, process, wrangle, and manipulate data with Machine Learning tools.
• Understand how to craft a CV suitable for a Machine Learning-focused data science role.
• Grasp the nuances of NumPy for numerical data in the context of Machine Learning.
• Delve into the intricacies of various Python libraries, like Pandas, Matplotlib, SciPy, and others, for Machine Learning applications.
• Navigate through key Machine Learning concepts and algorithms.
• Learn to devise bespoke data solutions using Machine Learning.

Reed Courses Certificate of Completion

Digital certificate - Included

• Python for Data Science and Machine Learning: Course Intro 1:11:56

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

• Data Science & Machine Learning Concepts 1:06:35

  • 8: Machine Learning: Why We Use Python 03:14
  • 9: Machine Learning: What is Data Science 13:24
  • 10: Machine Learning: What is Machine Learning 14:22
  • 11: Machine Learning: ML Concepts _ Algorithms 14:42
  • 12: Machine Learning: Machine Learning vs Deep Learning 11:09
  • 13: Machine Learning: What is Deep Learning 09:44

• Python For Data Science 2:51:38

  • 14: Machine Learning: What is Python Programming 06:03
  • 15: Machine Learning: Why Python for Data Science 04:35
  • 16: Machine Learning: What is Jupyter 03:54
  • 17: Machine Learning: What is Colab 03:27
  • 18: Machine Learning: Jupyter Notebook 18:01
  • 19: Machine Learning: Getting Started with Colab 09:07
  • 20: Machine Learning: Python Variables, Booleans and None 11:47
  • 21: Machine Learning: Python Operators 25:26
  • 22: Machine Learning: Python Numbers and Booleans 07:47
  • 23: Machine Learning: Python For Loops and While Loops 08:07
  • 24: Machine Learning: Python Lists 05:10
  • 25: Machine Learning: Python Tuples 11:25
  • 26: Machine Learning: Python Sets 09:41
  • 27: Machine Learning: Compound Data Types and When to use each Data Type 12:58
  • 28: Machine Learning: Functions 14:23
  • 29: Machine Learning: Python Object Oriented Programming 18:47
  • 30: Care for a feedback? 01:00

• Statistics for Data Science 58:30

  • 31: Machine Learning: Intro to Statistics 07:10
  • 32: Machine Learning: Descriptive Statistics 06:35
  • 33: Machine Learning: Measure of Variability 12:19
  • 34: Machine Learning: Measures of Variable Relationship 07:37
  • 35: Machine Learning: Inferential Statistics 15:18
  • 36: Machine Learning: Measures of Asymmetry 01:57
  • 37: Machine Learning: Sampling Distribution 07:34

• Probability and Hypothesis Testing 20:46

  • 38: Machine Learning: What Exactly Probability 03:44
  • 39: Machine Learning: Expected Values 02:38
  • 40: Machine Learning: Relative Frequency 05:15
  • 41: Machine Learning: Hypothesis Testing Overview 09:09

• NumPy Data Analysis 52:30

  • 42: Machine Learning: NumPy Array Data Types 12:58
  • 43: Machine Learning: NumPy Arrays 08:21
  • 44: Machine Learning: NumPy Array Basics 11:36
  • 45: Machine Learning: NumPy Array Indexing 09:10
  • 46: Machine Learning: NumPy Array Computations 05:53
  • 47: Machine Learning: Broadcasting 04:32

• Pandas Data Analysis 33:57

  • 48: Machine Learning: Intro to Pandas 15:52
  • 49: Machine Learning: Intro to Panda Continued 18:05

• Python Data Visualization 37:37

  • 50: Machine Learning: Data Visualization Overview 24:49
  • 51: Machine Learning: Different Data Visualization Libraries in Python 12:48

• Introduction to Machine Learning 26:03

  • 52: Machine Learning: Intro to ML 26:03

• Data Loading & Exploration 13:05

  • 53: Machine Learning: Exploratory Data Analysis 13:05

• Data Cleaning 15:23

  • 54: Machine Learning: Feature Scaling 07:40
  • 55: Machine Learning: Data Cleaning 07:43

• Feature Selecting and Engineering 06:11

  • 56: Machine Learning: Feature Engineering 06:11

• Linear and Logistic Regression 49:16

  • 57: Machine Learning: Linear Regression Intro 08:17
  • 58: Machine Learning: Gradient Descent 05:58
  • 59: Machine Learning: Linear Regression + Correlation Methods 26:33
  • 60: Machine Learning: Linear Regression Implemenation 05:06
  • 61: Machine Learning: Logistic Regression 03:22

• K Nearest Neighbours 1:36:41

  • 62: Machine Learning: KNN Overview 03:01
  • 63: Machine Learning: Parametic vs Non-Parametic Models 03:28
  • 64: Machine Learning: EDA on Iris Dataset 22:08
  • 65: Machine Learning: KNN - Intuition 02:16
  • 66: Machine Learning: Implement the KNN algorithm from scratch 11:45
  • 67: Machine Learning: Compare the Reuslt with Sklearn Library 03:47
  • 68: Machine Learning: KNN Hyperparameter tuning using the cross-validation 10:47
  • 69: Machine Learning: The decision boundary visualization 04:55
  • 70: Machine Learning: KNN - Manhattan vs Euclidean Distance 11:20
  • 71: Machine Learning: KNN Scaling in KNN 06:01
  • 72: Machine Learning: Curse of dimensionality 08:09
  • 73: Machine Learning: KNN use cases 03:32
  • 74: Machine Learning: KNN pros and cons 05:32

• Decision Trees 2:29:39

  • 75: Machine Learning: Decision Trees Section Overview 04:11
  • 76: Machine Learning: EDA on Adult Dataset 16:53
  • 77: Machine Learning: What is Entropy and Information Gain 21:50
  • 78: Machine Learning: The Decision Tree ID3 algorithm from scratch Part 1 11:32
  • 79: Machine Learning: The Decision Tree ID3 algorithm from scratch Part 2 07:35
  • 80: Machine Learning: The Decision Tree ID3 algorithm from scratch Part 3 04:07
  • 81: Machine Learning: Evaluating our ID3 implementation 16:51
  • 82: Machine Learning: Compare with Sklearn implementation 08:51
  • 83: Machine Learning: Visualizing the Tree 10:15
  • 84: Machine Learning: Plot the features importance 05:51
  • 85: Machine Learning: Decision Trees Hyper-parameters 11:39
  • 86: Machine Learning: Pruning 17:11
  • 87: Machine Learning: [Optional] Gain Ration 02:49
  • 88: Machine Learning: Decision Trees Pros and Cons 07:31
  • 89: Machine Learning: [Project] Predict whether income exceeds $50Kyr - Overview 02:33

• Ensemble Learning and Random Forests 14:16

  • 90: Machine Learning: Random Forests Pros and Cons 05:25
  • 91: Machine Learning: What is Boosting 04:41
  • 92: Machine Learning: AdaBoost Part 1 04:10

• Support Vector Machines 1:28:58

  • 93: Machine Learning: SVM - Outline 05:15
  • 94: Machine Learning: SVM - SVM intuition 11:38
  • 95: Machine Learning: SVM - Hard vs Soft Margin 13:25
  • 96: Machine Learning: SVM - C HP 04:17
  • 97: Machine Learning: SVM - Kernel Trick 12:18
  • 98: Machine Learning: SVM - Kernel Types 18:13
  • 99: Machine Learning: SVM - Linear Dataset 13:35
  • 100: Machine Learning: SVM with Regression 05:51
  • 101: Machine Learning: SVM - Project Overview 04:26

• K-Means 1:00:15

  • 102: Machine Learning: Unsupervised Machine Learning Intro 20:22
  • 103: Machine Learning: Representation of Clusters 20:48
  • 104: Machine Learning: Data Standardization 19:05

• PCA 2:00:12

  • 105: Machine Learning: PCA - Section Overview 05:12
  • 106: Machine Learning: What is PCA 09:36
  • 107: Machine Learning: PCA - Drawbacks 03:31
  • 108: Machine Learning: PCA - Algorithm Steps 13:12
  • 109: Machine Learning: PCA - Cov vs SVD 04:58
  • 110: Machine Learning: PCA - Main Applications 02:50
  • 111: Machine Learning: PCA - Image Compression Scratch 27:00
  • 112: Machine Learning: PCA - Data Preprocessing Scratch 14:31
  • 113: Machine Learning: PCA - BiPlot 17:27
  • 114: Machine Learning: PCA - Feature Scaling and Screeplot 09:29
  • 115: Machine Learning: PCA - Supervised vs unsupervised 04:55
  • 116: Machine Learning: PCA - Visualization 07:31

• Data Science Career 36:09

  • 117: Machine Learning: Creating a Data Science Resume 06:45
  • 118: Machine Learning: Data Science Cover Letter 03:33
  • 119: Machine Learning: How To Contact Recruiters 04:20
  • 120: Machine Learning: Getting Started with Freelancing 04:13
  • 121: Machine Learning: Top Freelance Websites 05:35
  • 122: Machine Learning: Personal Branding 04:02
  • 123: Machine Learning: Networking Do_s and Don_ts 03:45
  • 124: Machine Learning: Importance of a Website 02:56
  • 125: Thank You ! 01:00

• Python for Data Science & Machine Learning: Final Exam 03:00

  • 126: Python for Data Science & Machine Learning: Final Exam 03:00

Embark on a comprehensive journey into the world of data science and machine learning with our course, "Python for Data Science and Machine Learning from A-Z." Master the art of using Python for data analysis, as well as statistics and probability essential for data science. Delve into hypothesis testing and gain insights into interpreting results. Explore NumPy and Pandas libraries for in-depth data analysis and manipulation. Uncover the power of Python data visualization to effectively communicate insights. Our expert-led program equips you with essential techniques to excel in machine learning.

The Machine Learning Online Course is an industrial standard e-learning course. We've separated the course into multiple easily digestible modules, covering all essential elements of Machine Learning. Is there anything else included in this Machine Learning course package?

• We're a UK-based Training Provider accredited by CPD Group and registered with UKRLP.

• Study at your own pace from any device in our modern learning environment.

• Our exams test your knowledge and help you refine your skills on Machine Learning.

• You can get the PDF certificates from this Machine Learning for free!

• Most importantly, we will aid you in adapting to the updated industry compliance and practices regarding Machine Learning.

• Support is available for any questions you might have regarding any course content, not just Machine Learning.

Enrol now to immerse yourself in the world of impactful "Machine Learning." Don't miss this opportunity to become a machine learning expert through our comprehensive course. Join us to unlock your potential for harnessing Python's prowess for data science, predictive analysis, and informed decision-making that drives innovation.

There are no hidden fees, no sudden exam charges, and no other kind of unexpected payments.

The purpose of this Machine Learning is to assist learners in moving forward in their personal and professional lives.

This Machine Learning Training does not have any prerequisites or formal requirements.

This Machine Learning Masterclass can help you excel in your career in various ways.

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