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Machine Learning (beginner to expert)
Uplatz

Self-paced videos, Lifetime access, Study material, Certification prep, Technical support, Course Completion Certificate

Summary

Price
£100 inc VAT
Or £33.33/mo. for 3 months...
Study method
Online, On Demand
Duration
63.5 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Certificate of completion - Free
  • Reed courses certificate of completion - Free

Overview

Uplatz offers this comprehensive course on Machine Learning. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Machine Learning provides machines with the ability to learn autonomously based on experiences, observations and analyzing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow.

Whereas in machine learning, we input a data set through which the machine will learn by identifying and analyzing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset. The first step in machine learning basics is that we feed knowledge/data to the machine; this data is divided into two parts namely, training data and testing data.

In this Machine Learning Basics Course you will -

  • Get introduced to the world of machine learning with some basic concepts
  • Statistics, Artificial Intelligence, Deep Learning and Data mining are few of the other technical words used with machine learning
  • Learn about the different types of machine learning algorithms

At the end of the course you will receive a course completion certificate issued by Uplatz.

Certificates

Certificate of completion

Digital certificate - Included

Course Completion Certificate by Uplatz

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Curriculum

1
section
161
lectures
63h 31m
total

    • 1: 1. INTRODUCTION TO LINEAR ALGEBRA 32:52
    • 2: 1.1 INTRODUCTION TO LINEAR ALGEBRA 27:09
    • 3: 1.2 LINEAR ALGEBRA 1 13:55
    • 4: 1.2.1 LINEAR ALGEBRA 2 13:39
    • 5: 1.2.2 LINEAR ALGEBRA 3 11:21
    • 6: 1.2.3 LINEAR ALGEBRA 4.1 06:46
    • 7: 1.2.4 LINEAR ALGEBRA 4 08:40
    • 8: 1.2.5 LINEAR ALGEBRA 5.1 16:32
    • 9: 1.2.6 LINEAR ALGEBRA 5 16:45
    • 10: 1.2.7 LINEAR ALGEBRA 27:37
    • 11: 1.2.8 LINEAR ALGEBRA 16:27
    • 12: 1.2.9 LINEAR ALGEBRA 16:24
    • 13: 1.2.9.1 LINEAR ALGEBRA 25:27
    • 14: 1.2.9.2 LINEAR ALGEBRA 25:03
    • 15: 1.2.9.2.1 LINEAR ALGEBRA 16:45
    • 16: 2.1 INTRODUCTION TO PYTHON 16:19
    • 17: 2.1.1 INTRODUCTION TO PYTHON 19:20
    • 18: 2.1.2 PYTHON DATATYPES 18:51
    • 19: 2.1.3 PYTHON OPERATORS 21:37
    • 20: 2.1.4 ADVANCED DATA TYPES 18:40
    • 21: 2.1.5 SIMPLE PYTHON PROGRAM 17:47
    • 22: 2.1.6 PYTHON CONDITION STATEMENTS 25:44
    • 23: 2.1.7 PYTHON LOOPING STATEMENTS 16:48
    • 24: 2.1.8 - BREAK AND CONTINUE KEYWORDS IN PYTHON 20:49
    • 25: 2.1.9 FUNCTIONS IN PYTHON 16:53
    • 26: 2.1.9.1 FUNCTION ARGUMENTS 18:40
    • 27: 2.1.9.2 FUNCTION REQUIRED ARGUMENTS 10:36
    • 28: 2.1.9.3 DEFAULT ARGUMENTS 06:37
    • 29: 2.1.9.4 VARIABLE ARGUMENTS 32:18
    • 30: 2.2 BUILT IN FUNCTIONS 37:15
    • 31: 2.2.1 BUILT IN FUNCTIONS 09:45
    • 32: 2.2.2 SCOPE OF VARIABLES 13:15
    • 33: 2.2.3 PYTHON MATH MODULE 12:53
    • 34: 2.2.3.1 PYTHON MATH MODULE 18:40
    • 35: 2.2.4 PYTHON MATPLOTLIB MODULE 1:04:10
    • 36: 2.2.5 A BASIC GUI APPLICATION 33:37
    • 37: 2.2.6 A BASIC GUI APPLICATION 36:44
    • 38: 2.3 NUMPY BASICS 17:06
    • 39: 2.3.1 NUMPY BASICS 24:43
    • 40: 2.3.2 NUMPY BASICS 29:55
    • 41: 2.3.3 NUMPY BASICS 28:44
    • 42: 2.3.4 NUMPY BASICS 21:25
    • 43: 2.3.5 NUMPY BASICS 21:26
    • 44: 2.3.6 NUMPY BASICS 22:47
    • 45: 2.3.7 NUMPY BASICS 16:53
    • 46: 2.3.8 NUMPY BASICS 10:24
    • 47: 2.3.9 NUMPY BASICS 10:23
    • 48: 2.3.9.1 NUMPY BASICS 14:44
    • 49: 2.3.9.2 NUMPY BASICS 06:22
    • 50: 2.3.9.3 NUMPY BASICS 14:16
    • 51: 2.3.9.4 NUMPY BASICS 25:20
    • 52: 2.3.9.5 NUMPY BASICS 14:24
    • 53: 2.3.9.6 NUMPY BASICS 18:25
    • 54: 2.3.9.7 NUMPY BASICS 14:41
    • 55: 2.3.9.8 NUMPY BASICS 16:35
    • 56: 2.3.9.9 NUMPY BASICS 29:19
    • 57: 2.3.9.11 NUMPY BASICS 24:23
    • 58: 2.3.9.12 NUMPY BASICS 15:39
    • 59: 2.3.9.13 NUMPY BASICS 25:20
    • 60: 2.3.9.14 NUMPY BASICS 22:12
    • 61: 2.3.9.15 NUMPY BASICS 19:44
    • 62: 2.3.9.16 NUMPY BASICS 21:46
    • 63: 2.3.9.17 NUMPY BASICS 21:24
    • 64: 2.3.9.18 NUMPY BASICS 11:54
    • 65: 2.3.9.19 NUMPY BASICS 12:18
    • 66: 2.3.9.21 NUMPY BASICS 16:35
    • 67: 2.4 FILE SYSTEM 14:09
    • 68: 2.4.1 FILE SYSTEM WITH STATEMENT 23:38
    • 69: 2.4.2 FILE SYSTEM READ AND WRITE 28:56
    • 70: 2.5 RANDOM MODULE BASICS 14:49
    • 71: 2.5.1 RANDOM MODULE BASICS 19:02
    • 72: 2.5.2 RANDOM MODULE BASICS 22:42
    • 73: 2.5.3 RANDOM MODULE BASICS 22:42
    • 74: 2.5.4 RANDOM MODULE BASICS 10:04
    • 75: 2.5.5 RANDOM MODULE BASICS 09:21
    • 76: 2.5.6 RANDOM MODULE BASICS 43:00
    • 77: 2.6 PANDAS BASICS 15:57
    • 78: 2.6.1 PANDAS BASICS 05:54
    • 79: 2.6.2 PANDAS BASICS 32:00
    • 80: 2.6.3 PANDAS BASICS 20:59
    • 81: 2.6.4 PANDAS BASICS 28:10
    • 82: 2.6.5 PANDAS BASICS 15:19
    • 83: 2.6.6 PANDAS BASICS 08:09
    • 84: 2.6.7 PANDAS BASICS 18:57
    • 85: 2.7 MATPLOTLIB BASICS 20:23
    • 86: 2.7.1 MATPLOTLIB BASICS 20:38
    • 87: 2.7.2 MATPLOTLIB BASICS 17:32
    • 88: 2.7.3 MATPLOTLIB BASICS 04:00
    • 89: 2.7.4 MATPLOTLIB BASICS 11:32
    • 90: 2.7.5 MATPLOTLIB BASICS 07:24
    • 91: 2.7.6 MATPLOTLIB BASICS 16:55
    • 92: 2.7.7 MATPLOTLIB BASICS 11:54
    • 93: 2.7.8 MATPLOTLIB BASICS 17:17
    • 94: 2.7.9 MATPLOTLIB BASICS 17:40
    • 95: 2.7.9.1 MATPLOTLIB BASICS 16:55
    • 96: 2.7.9.11 MATPLOTLIB BASICS 20:38
    • 97: 2.8 AGE CALCULATOR APP 26:46
    • 98: 2.8.1 AGE CALCULATOR APP 12:22
    • 99: 2.8.2 AGE CALCULATOR APP 33:00
    • 100: 2.8.3 AGE CALCULATOR APP 37:57
    • 101: 3.1 MACHINE LEARNING BASICS 27:27
    • 102: 3.1.1 MACHINE LEARNING BASICS 17:31
    • 103: 3.1.2 MACHINE LEARNING BASICS 17:36
    • 104: 3.1.3 MACHINE LEARNING BASICS 15:39
    • 105: 3.1.4 MACHINE LEARNING BASICS 13:54
    • 106: 3.1.5 MACHINE LEARNING BASICS 11:56
    • 107: 3.1.6 MACHINE LEARNING BASICS 18:52
    • 108: 3.1.7 MACHINE LEARNING BASICS 23:56
    • 109: 3.1.8 MACHINE LEARNING BASICS 22:38
    • 110: 3.1.9 MACHINE LEARNING BASICS 29:13
    • 111: 3.1.9.1 MACHINE LEARNING BASICS 17:36
    • 112: 3.2 MACHINE LEARNING BASICS 08:09
    • 113: 4.1 TYPES OF MACHINE LEARNING 35:12
    • 114: 4.1.1 TYPES OF MACHINE LEARNING 15:03
    • 115: 4.1.2 TYPES OF MACHINE LEARNING 16:18
    • 116: 4.1.3 TYPES OF MACHINE LEARNING 13:58
    • 117: 4.1.4 TYPES OF MACHINE LEARNING 19:45
    • 118: 4.1.5 TYPES OF MACHINE LEARNING 05:13
    • 119: 4.1.6 TYPES OF MACHINE LEARNING 31:39
    • 120: 5.1 TYPES OF MACHINE LEARNING 28:20
    • 121: 5.1.1 TYPES OF MACHINE LEARNING 31:56
    • 122: 5.1.2 TYPES OF MACHINE LEARNING 25:09
    • 123: 5.1.3 TYPES OF MACHINE LEARNING 46:37
    • 124: 5.1.4 TYPES OF MACHINE LEARNING 31:00
    • 125: 5.1.5 TYPES OF MACHINE LEARNING 24:22
    • 126: 5.1.6 TYPES OF MACHINE LEARNING 16:20
    • 127: 5.1.7 TYPES OF MACHINE LEARNING 32:54
    • 128: 5.1.8 TYPES OF MACHINE LEARNING 56:20
    • 129: 5.2 MULTIPLE REGRESSION 34:30
    • 130: 5.2.1 MULTIPLE REGRESSION 37:36
    • 131: 5.2.2 MULTIPLE REGRESSION 40:56
    • 132: 5.2.3 MULTIPLE REGRESSION 56:04
    • 133: 5.2.4 MULTIPLE REGRESSION 46:42
    • 134: 5.2.5 MULTIPLE REGRESSION 38:14
    • 135: 5.2.6 MULTIPLE REGRESSION 37:49
    • 136: 5.2.7 MULTIPLE REGRESSION 1:01:26
    • 137: 5.3 KNN INTRO 26:49
    • 138: 5.3.1 KNN ALGORITHM 48:57
    • 139: 5.3.2 KNN ALGORITHM 11:17
    • 140: 5.3.3 INTRODUCTION TO CONFUSION MATRIX 42:22
    • 141: 5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT 24:37
    • 142: 5.3.5 KNN ALGORITHM 50:29
    • 143: 5.3.6 KNN ALGORITHM 56:11
    • 144: 5.4 INTRODUCTION TO DECISION TREE 44:37
    • 145: 5.4.1 INTRODUCTION TO DECISION TREE 39:32
    • 146: 5.4.2 DECISION TREE ALGORITHM 36:42
    • 147: 5.4.3 DECISION TREE ALGORITHM 20:11
    • 148: 5.4.4 DECISION TREE ALGORITHM 55:38
    • 149: 5.5 UNSUPERVISED LEARNING 23:26
    • 150: 5.5.1 UNSUPERVISED LEARNING 09:17
    • 151: 5.5.2 UNSUPERVISED LEARNING 18:29
    • 152: 5.5.3 UNSUPERVISED LEARNING 29:50
    • 153: 5.5.4 AHC ALGORITHM 46:31
    • 154: 5.5.5 AHC ALGORITHM 19:56
    • 155: 5.6 KMEANS CLUSTERING 23:09
    • 156: 5.6.1 KMEANS CLUSTERING 30:26
    • 157: 5.6.2 KMEANS CLUSTERING 1:01:05
    • 158: 5.6.3 DBSCAN ALGORITHM 37:09
    • 159: 5.6.4 DBSCAN PROGRAM 32:45
    • 160: 5.6.5 DBSCAN PROGRAM 49:57
    • 161: Machine Learning with Python - Interview Questions 05:00

Course media

Description

Machine Learning Basics – Course Syllabus

Introduction to Machine Learning

  • Understanding Machine Learning Concepts

  • Key Applications of ML in the Real World

  • Machine Learning vs. Artificial Intelligence

  • Introduction to AI and Intelligent Agents

  • Overview of Popular Python Libraries for ML

  • Common Interview Questions for Beginners

Working with Data

  • Importance of Data in ML

  • Data Processing Fundamentals

  • Generating Test Datasets with Scikit-learn

  • Data Preprocessing Techniques in Python

    • Cleansing, Scaling, and Transformation

    • Label Encoding and One-Hot Encoding

  • Handling Imbalanced Datasets

    • SMOTE and Near Miss Algorithms

Supervised Learning Foundations

  • Overview: Classification and Regression

  • Comparing Supervised Learning Methods

  • Multi-class Classification with Scikit-learn

Gradient Descent & Optimization

  • Fundamentals of Gradient Descent

  • Stochastic & Mini-Batch Gradient Descent

  • Optimization Enhancements: Momentum, Learning Rates

Regression Techniques

  • Linear Regression

    • Theory, Equations, and Implementations

    • Using Python, R, TensorFlow, PyTorch, Scikit-learn, and PySpark

    • Case Study: Boston Housing Dataset

  • Polynomial Regression

    • Concepts and Implementation in Python

  • Softmax Regression

    • Using TensorFlow for Multi-class Classification

Logistic Regression

  • Introduction and Role in Classification

  • Mathematical Foundations and Cost Function

  • Implementation in Python and TensorFlow

Classification Algorithms

  • Naive Bayes Classifier – Concepts and Use Cases

  • Support Vector Machines (SVM)

    • Implementation in Python and R

    • Hyperparameter Tuning with GridSearchCV

    • Handling Non-linear Datasets

  • Decision Trees

    • Concepts, Examples, and Practical Implementation

    • Use in Regression and Classification

  • Random Forest and Ensemble Methods

    • Random Forest Regression

    • Bagging and Voting Classifiers with Scikit-learn

Who is this course for?

  • Beginners in Machine Learning and AI who want to build a foundational understanding of core ML concepts.
  • Students and recent graduates from computer science, engineering, mathematics, or statistics backgrounds looking to enter the data science field.
  • Data analysts and business intelligence professionals transitioning into machine learning roles.
  • Software developers and engineers interested in integrating machine learning models into applications.
  • IT professionals aiming to upskill or reskill for AI/ML-focused career paths.
  • Researchers and academicians seeking structured training in applied ML techniques.
  • Anyone preparing for job interviews or certification exams in data science, AI, or ML-related domains.

Requirements

Passion to achieve your goals

Career path

  • Machine Learning Engineer
  • Data Scientist
  • AI/ML Intern
  • Data Analyst – ML Focus
  • Python Developer – ML Applications
  • Research Assistant – Machine Learning
  • Junior Machine Learning Engineer
  • Business Intelligence Analyst (with ML skills)
  • AI/ML Trainee
  • Data Science Intern

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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

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