Machine Learning and Data Science Masterclass with Python - CPD Certified

Machine Learning and Data Science Masterclass with Python - CPD Certified

Embark on a transformative journey into the world of Machine Learning with our comprehensive Machine Learning course. From mastering Python for Data Science & Machine Learning to delving into Statistics, Probability, and advanced techniques like Ensemble Learning, this course is your gateway to a rewarding career in Machine Learning. Don't miss out on the opportunity to become a skilled Machine Learning practitioner – enrol today!

Key Performance Metrics in Machine Learning and Data Science Masterclass with Python:

• Apply core Machine Learning principles to real-world data science challenges.
• Analyse data using relevant tools and techniques for Machine Learning.
• Proficiently use Python for Machine Learning tasks.
• Apply statistical methods to derive insights for Machine Learning.
• Use probability and hypothesis testing in Machine Learning decision-making.
• Employ NumPy and Pandas for data tasks in Machine Learning.
• Create impactful Python visualisations for Machine Learning results.
• Understand key Machine Learning algorithms and their applications.
• Optimise data for Machine Learning through feature selection and engineering.
• Choose appropriate models for Machine Learning based on data and goals.

Accreditation

This Machine Learning and Data Science Masterclass with Python Course is CPDQE Accredited, which serves as an impactful mechanism for skill enhancement.

Perks:

• Dual Certificate
• 30 CPD Points
• 24/7 Tutor Support

CPDQE Certificate on Machine Learning and Data Science Masterclass

Digital certificate - Included

Official Transcript of Machine Learning and Data Science Masterclass

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included

• Python for Data Science & Machine Learning from A-Z 1:12:56

  • 1: Machine Learning and Data Science: Who is this Course for Preview 02:43
  • 2: Machine Learning and Data Science: DS + ML Marketplace Preview 06:55
  • 3: Machine Learning and Data Science: Data Science Job Opportunities Preview 04:24
  • 4: Machine Learning and Data Science: 1.4 Data Science Job Roles 10:23
  • 5: Machine Learning and Data Science: What is a Data Scientist 17:00
  • 6: Machine Learning and Data Science: How To Get a Data Science Job 18:39
  • 7: Machine Learning and Data Science: Data Science Projects Overview 11:52
  • 8: Machine Learning and Data Science: Section 1 MCQ 01:00

• Data Science & Machine Learning Concepts 1:07:35

  • 9: Machine Learning and Data Science: Why We Use Python 03:14
  • 10: Machine Learning and Data Science: What is Data Science 13:24
  • 11: Machine Learning and Data Science: What is Machine Learning 14:22
  • 12: Machine Learning and Data Science: ML Concepts _ Algorithms 14:42
  • 13: Machine Learning and Data Science: Machine Learning vs Deep Learning 11:09
  • 14: Machine Learning and Data Science: What is Deep Learning 09:44
  • 15: Machine Learning and Data Science: Section 2 MCQ 01:00

• Python For Data Science 3:54:10

  • 16: Machine Learning and Data Science: What is Python Programming 06:03
  • 17: Machine Learning and Data Science: Why Python for Data Science 04:35
  • 18: Machine Learning and Data Science: What is Jupyter 03:54
  • 19: Machine Learning and Data Science: What is Colab 03:27
  • 20: Machine Learning and Data Science: Jupyter Notebook 18:01
  • 21: Machine Learning and Data Science: Getting Started with Colab 09:07
  • 22: Machine Learning and Data Science: Python Variables, Booleans and None 11:47
  • 23: Machine Learning and Data Science: Python Operators 25:26
  • 24: Machine Learning and Data Science: Python Numbers and Booleans 07:47
  • 25: Machine Learning and Data Science: Python Strings 13:12
  • 26: Machine Learning and Data Science: Python Conditional Statements 13:53
  • 27: Machine Learning and Data Science: Python For Loops and While Loops 08:07
  • 28: Machine Learning and Data Science: Python Lists 05:10
  • 29: Machine Learning and Data Science: More About Python Lists 15:08
  • 30: Machine Learning and Data Science: Python Tuples 11:25
  • 31: Machine Learning and Data Science: Python Dictionaries 20:19
  • 32: Machine Learning and Data Science: Python Sets 09:41
  • 33: Machine Learning and Data Science: Compound Data Types 12:58
  • 34: Machine Learning and Data Science: Functions 14:23
  • 35: Machine Learning and Data Science: Python Object Oriented Programming 18:47
  • 36: Machine Learning and Data Science: Section 3 MCQ 01:00

• Statistics for Data Science 1:09:05

  • 37: Machine Learning and Data Science: Intro to Statistics 07:10
  • 38: Machine Learning and Data Science: Descriptive Statistics 06:35
  • 39: Machine Learning and Data Science: Measure of Variability 12:19
  • 40: Machine Learning and Data Science: Measure of Variability Continued 09:35
  • 41: Machine Learning and Data Science: Measures of Variable Relationship 07:37
  • 42: Machine Learning and Data Science: Inferential Statistics 15:18
  • 43: Machine Learning and Data Science: Measures of Asymmetry 01:57
  • 44: Machine Learning and Data Science: Sampling Distribution 07:34
  • 45: Machine Learning and Data Science: Section 4 MCQ 01:00

• Probability and Hypothesis Testing 21:46

  • 46: Machine Learning and Data Science: What Exactly Probability 03:44
  • 47: Machine Learning and Data Science: Expected Values 02:38
  • 48: Machine Learning and Data Science: Relative Frequency 05:15
  • 49: Machine Learning and Data Science: Hypothesis Testing Overview 09:09
  • 50: Machine Learning and Data Science: Section 5 MCQ 01:00

• NumPy Data Analysis 52:30

  • 51: Machine Learning and Data Science: NumPy Array Data Types 12:58
  • 52: Machine Learning and Data Science: NumPy Arrays Preview 08:21
  • 53: Machine Learning and Data Science: NumPy Array Basics 11:36
  • 54: Machine Learning and Data Science: NumPy Array Indexing Preview 09:10
  • 55: Machine Learning and Data Science: NumPy Array Computations 05:53
  • 56: Machine Learning and Data Science: Broadcasting 04:32

• Pandas Data Analysis 33:57

  • 57: Machine Learning and Data Science: Pandas 15:52
  • 58: Machine Learning and Data Science: Panda Continued 18:05

• Python Data Visualization 47:04

  • 59: Machine Learning and Data Science: Data Visualization Overview 24:49
  • 60: Machine Learning and Data Science: Data Visualization Libraries in Python 12:48
  • 61: Machine Learning and Data Science: Python Data Visualization Implementation 08:27
  • 62: Machine Learning and Data Science: Section 8 MCQ 01:00

• Introduction to Machine Learning 26:03

  • 63: Machine Learning and Data Science: Intro to ML 26:03

• Data Loading & Exploration 13:05

  • 64: Machine Learning and Data Science: Exploratory Data Analysis 13:05

• Data Cleaning 16:23

  • 65: Machine Learning and Data Science: Feature Scaling 07:40
  • 66: Machine Learning and Data Science: Data Cleaning 07:43
  • 67: Machine Learning and Data Science: Section 11 MCQ 01:00

• Feature Selecting and Engineering 06:11

  • 68: Machine Learning and Data Science: Feature Engineering 06:11

• Linear and Logistic Regression 49:16

  • 69: Machine Learning and Data Science: Linear Regression Intro 08:17
  • 70: Machine Learning and Data Science: Gradient Descent 05:58
  • 71: Machine Learning and Data Science: Linear Regression + Correlation Methods 26:33
  • 72: Machine Learning and Data Science: Linear Regression Implemenation 05:06
  • 73: Machine Learning and Data Science: Logistic Regression 03:22

• K Nearest Neighbours 1:36:41

  • 74: Machine Learning and Data Science: KNN Overview 03:01
  • 75: Machine Learning and Data Science: Parametic vs Non-Parametic Models 03:28
  • 76: Machine Learning and Data Science: EDA on Iris Dataset 22:08
  • 77: Machine Learning and Data Science: KNN - Intuition 02:16
  • 78: Machine Learning and Data Science: Implement the KNN algorithm from scratch 11:45
  • 79: Machine Learning and Data Science: Compare the Reuslt with Sklearn Library 03:47
  • 80: Machine Learning and Data Science: KNN Hyperparameter tuning 10:47
  • 81: Machine Learning and Data Science: The decision boundary visualization 04:55
  • 82: Machine Learning and Data Science: Manhattan vs Euclidean Distance 11:20
  • 83: Machine Learning and Data Science: KNN Scaling in KNN 06:01
  • 84: Machine Learning and Data Science: Curse of dimensionality 08:09
  • 85: Machine Learning and Data Science: KNN use cases 03:32
  • 86: Machine Learning and Data Science: KNN pros and cons 05:32

• Decision Trees 2:51:02

  • 87: Machine Learning and Data Science: Decision Trees Section Overview 04:11
  • 88: Machine Learning and Data Science: EDA on Adult Dataset 16:53
  • 89: Machine Learning and Data Science: What is Entropy and Information Gain 21:50
  • 90: Machine Learning and Data Science: Tree ID3 algorithm from scratch Part 1 11:32
  • 91: Machine Learning and Data Science: Tree ID3 algorithm from scratch Part 2 07:35
  • 92: Machine Learning and Data Science: Tree ID3 algorithm from scratch Part 3 04:07
  • 93: Machine Learning and Data Science: Putting Everything Together 21:23
  • 94: Machine Learning and Data Science: Evaluating our ID3 implementation 16:51
  • 95: Machine Learning and Data Science: Compare with Sklearn implementation 08:51
  • 96: Machine Learning and Data Science: Visualizing the Tree 10:15
  • 97: Machine Learning and Data Science: Plot the features importance 05:51
  • 98: Machine Learning and Data Science: Decision Trees Hyper-parameters 11:39
  • 99: Machine Learning and Data Science: Pruning 17:11
  • 100: Machine Learning and Data Science: [Optional] Gain Ration 02:49
  • 101: Machine Learning and Data Science: Decision Trees Pros and Cons 07:31
  • 102: Machine Learning and Data Science: Predict whether income exceeds $50Kyr 02:33

• Ensemble Learning and Random Forests 1:44:01

  • 103: Machine Learning and Data Science: Ensemble Learning Section Overview 03:46
  • 104: Machine Learning and Data Science: What is Ensemble Learning 13:06
  • 105: Machine Learning and Data Science: What is Bootstrap Sampling 08:25
  • 106: Machine Learning and Data Science: What is Bagging 05:20
  • 107: Machine Learning and Data Science: Out-of-Bag Error 07:47
  • 108: Machine Learning and Data Science: Implementing Random Forests from scratch P 1 22:34
  • 109: Machine Learning and Data Science: Implementing Random Forests from scratch P 2 06:10
  • 110: Machine Learning and Data Science: Compare with sklearn implementation 03:41
  • 111: Machine Learning and Data Science: Random Forests Hyper-Parameters 04:23
  • 112: Machine Learning and Data Science: Random Forests Pros and Cons 05:25
  • 113: Machine Learning and Data Science: What is Boosting 04:41
  • 114: Machine Learning and Data Science: Ada Boost Part 1 04:10
  • 115: Machine Learning and Data Science: Ada Boost Part 2 14:33

• Support Vector Machines 1:41:48

  • 116: Machine Learning and Data Science: SVM - Outline 05:15
  • 117: Machine Learning and Data Science: SVM - SVM intuition 11:38
  • 118: Machine Learning and Data Science: SVM - Hard vs Soft Margin 13:25
  • 119: Machine Learning and Data Science: SVM - C HP 04:17
  • 120: Machine Learning and Data Science: SVM - Kernel Trick 12:18
  • 121: 17.6 SVM - Kernel Types 18:13
  • 122: Machine Learning and Data Science: SVM - Linear Dataset 13:35
  • 123: Machine Learning and Data Science: SVM - Non-Linear Dataset 12:50
  • 124: Machine Learning and Data Science: SVM with Regression 05:51
  • 125: Machine Learning and Data Science: SVM - Project Overview 04:26

• K-Means 1:00:15

  • 126: Machine Learning and Data Science: Unsupervised Machine Learning Intro 20:22
  • 127: Machine Learning and Data Science: Representation of Clusters 20:48
  • 128: Machine Learning and Data Science: Data Standardization 19:05

• PCA 2:01:12

  • 129: Machine Learning and Data Science: PCA - Section Overview 05:12
  • 130: Machine Learning and Data Science: What is PCA 09:36
  • 131: Machine Learning and Data Science: PCA - Drawbacks 03:31
  • 132: Machine Learning and Data Science: PCA - Algorithm Steps 13:12
  • 133: Machine Learning and Data Science: PCA - Cov vs SVD 04:58
  • 134: Machine Learning and Data Science: PCA - Main Applications 02:50
  • 135: Machine Learning and Data Science: PCA - Image Compression Scratch 27:00
  • 136: Machine Learning and Data Science: PCA - Data Preprocessing Scratch 14:31
  • 137: Machine Learning and Data Science: PCA - BiPlot 17:27
  • 138: Machine Learning and Data Science: PCA - Feature Scaling and Screeplot 09:29
  • 139: Machine Learning and Data Science: PCA - Supervised vs unsupervised 04:55
  • 140: Machine Learning and Data Science: PCA - Visualization 07:31
  • 141: Machine Learning and Data Science: Section 11 MCQ 01:00

• Data Science Career 35:09

  • 142: Machine Learning and Data Science: Creating a Data Science Resume 06:45
  • 143: Machine Learning and Data Science: Data Science Cover Letter 03:33
  • 144: Machine Learning and Data Science: How To Contact Recruiters 04:20
  • 145: Machine Learning and Data Science: Getting Started with Freelancing 04:13
  • 146: Machine Learning and Data Science: Top Freelance Websites 05:35
  • 147: Machine Learning and Data Science: Personal Branding 04:02
  • 148: Machine Learning and Data Science: Networking Do_s and Don_ts 03:45
  • 149: Machine Learning and Data Science: Importance of a Website 02:56

• Bonus Resource 36:00

  • 150: Machine Learning and Data Science: Importing Data 02:00
  • 151: Machine Learning and Data Science: Jupyter Notebook Preview 03:00
  • 152: Machine Learning and Data Science: Matplotlib 03:00
  • 153: Machine Learning and Data Science: NumPy Basics 03:00
  • 154: Machine Learning and Data Science: Pandas 03:00
  • 155: Machine Learning and Data Science: Python Basics 02:00
  • 156: Machine Learning and Data Science: Python For Data Science Cheatsheet 03:00
  • 157: Machine Learning and Data Science: Scikit-Learn 02:00
  • 158: Machine Learning and Data Science: Seaborn 02:00
  • 159: Machine Learning and Data Science: Supervised Learning 08:00
  • 160: Machine Learning and Data Science: Unsupervised Learning 05:00

• Career Resource and Templates 3:33:00

  • 161: Machine Learning and Data Science: Cold+Email+Template+_+Examples 02:00
  • 162: Machine Learning and Data Science: Cover Letter Sample 01:00
  • 163: Machine Learning and Data Science: Cover+Letter+Template+_+Samples 06:00
  • 164: Machine Learning and Data Science: Data Science 600 Questions and Answers 3:19:00
  • 165: Machine Learning and Data Science: networking+guide 01:00
  • 166: Machine Learning and Data Science: Recruiter Email Template 01:00
  • 167: Machine Learning and Data Science: Recruiter+Reach+Out+Template+and+Examples 01:00
  • 168: Machine Learning and Data Science: Resume Template 01:00
  • 169: Machine Learning and Data Science: Resume+Checklist 01:00

This comprehensive Machine Learning course embarks on an exciting journey into the realm of Machine Learning. From mastering Python for data science to delving into advanced algorithms like Random Forests and Support Vector Machines, you'll acquire the skills and knowledge necessary for a successful Machine Learning career. Elevate your data science capabilities and unlock opportunities for a promising future in Machine Learning with our program.

Exploring Data Science and Machine Learning Careers

Welcome to our journey into the world of data science and machine learning! In this section, we've prepared 7 engaging video lectures, with a total duration of 1 hour. We'll dive deep into the exciting realm of data science, exploring job roles, and uncovering numerous opportunities within this dynamic field.

Mastering Data Science & Machine Learning Concepts

In this section, we'll lay the foundation for your data science and machine learning journey. You'll grasp key concepts like data science, machine learning algorithms, deep learning, and more. We'll provide interactive content and quizzes to ensure you're not just learning but actively engaging with these fundamental ideas.

Python's Role in Data Science

Python is the backbone of data science, and in this extensive section, we have packed in 20 insightful lectures and interactive multiple-choice question all about Python for data science. Topics include essential tools like Jupyter and Colab, Python variables, Booleans, operators, numbers, strings, and conditional statements. You'll also delve into Python data structures, including lists, tuples, dictionaries, sets, various data types, functions, and even object-oriented programming. Get ready to harness the power of Python in your data science and machine learning journey!

Statistics: The Data Science Foundation

Unlock the power of statistics in data science! Our engaging section guides you through key statistical concepts, essential for mastering data science. From variability measures to inferential statistics, our interactive content makes complex ideas accessible and relevant for your data science aspirations.

Probability & Hypothesis Testing in Data Science

Essential for data science, our section on Probability and Hypothesis Testing deepens your understanding with interactive learning. Explore probability theories and hypothesis testing methods that are critical in data science, enhancing your analytical skills for machine learning challenges.

NumPy for Python Data Analysis

NumPy, the cornerstone of Python data analysis, is demystified in this focused section. Learn about array operations, computations, and broadcasting, crucial for efficient data handling in Python. Our practical approach empowers your data science projects with machine learning-ready data.

Python Data Visualization Techniques
Transform data into insights with our Python Data Visualization section. Discover powerful Python libraries and techniques to visualize data trends and patterns. Perfect for data science enthusiasts, this section enhances your analytical skills, crucial for impactful machine learning insights.

Data Cleaning for Accurate Data Science

Clean data is the bedrock of successful data science. Learn the art of data cleaning and feature scaling to ensure precision in your data science projects. Our section equips you with essential skills to prepare clean, machine-learning-ready datasets.

Linear & Logistic Regression: Machine Learning Basics

Grasp the fundamentals of machine learning with our Linear and Logistic Regression section. Understand Gradient Descent and Regression Analysis through Python, essential for practical machine learning applications in data science.

KNN: Machine Learning with Python

Explore KNN, a versatile machine learning algorithm, in our comprehensive guide. Learn about model types, distance metrics, and implementation using Python, enhancing your data science toolkit.

Decision Trees: Python Machine Learning Models

Dive into Decision Trees, a key machine-learning model in Python. Our section covers everything from entropy to ID3 algorithms, equipping you with the skills to implement and evaluate decision trees in your data science projects.

This Course is suitable for anyone interested to further progress there career in:

• Level 4 HNC in AI and Machine Learning
• Level 5 HND in AI and Machine Learning
• Level 6 BSc (Hons) in Machine Learning
• Level 7 MSc in Machine Learning
• Level 7 Professional Diploma in Machine Learning
• Level 7 Post Graduate Diploma in Machine Learning
• Level 7 MSc by Research in Machine Learning
• Level 8 PhD in Machine Learning
• Level 6 Certificate in Machine Learning
• Level 6 Diploma in Machine Learning
• Level 7 Certificate in Machine Learning
• Level 7 Diploma in Machine Learning
• Level 8 Certificate in Machine Learning
• Level 8 Diploma in Machine Learning
• Level 6 Graduate Diploma in Machine Learning
• Level 7 Graduate Diploma in Machine Learning
• Level 8 Graduate Diploma in Machine Learning
• Level 6 Postgraduate Diploma in Machine Learning
• Level 7 Postgraduate Diploma in Machine Learning
• Level 8 Postgraduate Diploma in Machine Learning
• Level 5 Diploma in Machine Learning and Data Science
• Level 6 Diploma in Data Science and Machine Learning
• Level 7 Diploma in Data Science and Machine Learning

This Machine Learning and Data Science Masterclass with Python is open to all.

Taking Machine Learning Course will open up a variety of career options for you.

• Data Scientist
• Machine Learning Engineer
• Excel Analyst
• Python Developer
• Software developer

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