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Machine Learning for Absolute Beginners – Level 2

13 STUDENTS ENROLLED

Overview

Our Machine Learning for Absolute Beginners – Level 2 course is devised to direct learners on the use of python and pandas library for data science projects on machine learning. Python is one of the most widely used programming language and the most popular programming language for machine learning. 

Pandas is a software library written for the Python programming used for data structures and operations for manipulating numerical tables and time series. The course will instruct learners with the theoretical and practical knowledge of applying these programs for data processing works to give a usable and desired form with the help of machine learning.

Applications and websites are using machine learning to recommend users better and convenient products. The scope of machine learning can be used in a huge array of fields ranging from data science, finance, medical research and lots more. As more and more companies are adopting such techniques Machine language skills are likely to create a lot of jobs in the future. 

 

Highlights of Machine Learning for Absolute Beginners – Level 2 Course

  • Learn how Python syntax is used for developing data science projects
  • Configure and use JupyterLab tool for Jupiter notebooks
  • Familiarize with the method of Loading large datasets from files using Pandas
  • Discover procedure of Loading and Analysing Tabular Datasets
  • Find out how to perform data analysis and exploration
  • Perform data cleaning and transformation as a pre-processing step before moving into machine learning algorithms

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

How is the course assessed?

To successfully complete the course you must pass an automated, multiple-choice assessment. The assessment is delivered through our online learning platform. You will receive the results of your assessment immediately upon completion.

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hardcopy Certificate for £15.

Why study this course

Machine Learning for Absolute Beginners – Level 2 Course is open to all, with no formal entry requirements. Completing this course will open up job opportunities in IT, data science, research analysis, finance, software and app development and so on. 

Requirements 

All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and be over the age of 16  to join this course.

Course Curriculum

Getting Started with Level 2!
Welcome 00:02:00
Anaconda Installation 00:04:00
JupyterLab Overview 00:04:00
Working with a Jupyter Notebook 00:14:00
Python Fundamentals for Data Science
Overview 00:03:00
Variables and Data Types 00:07:00
Strings 00:08:00
Lists 00:10:00
IF and For-Loop Statements 00:07:00
Functions 00:08:00
Dictionaries 00:11:00
Classes, Objects, Attributes, and Methods 00:07:00
Importing Modules 00:08:00
Libraries for Data Science Projects 00:07:00
Exercise #1 – Python Fundamentals 00:01:00
Introduction to the Pandas Library
Overview 00:03:00
Series Data Structure (1D) 00:13:00
DataFrame Data Structure (2D) 00:05:00
Data Selection in a DataFrame 00:15:00
Exercise #2 – Pandas Series and DataFrame 00:01:00
Loading Data into a DataFrame
Overview 00:01:00
Kaggle and the Titanic Dataset 00:06:00
Loading a Tabular Data File 00:07:00
Adjusting the Loading Parameters 00:13:00
Using Summary Statistics 00:05:00
The Concept of Methods Chaining 00:05:00
Sorting and Ranking 00:03:00
Filtering 00:05:00
Grouping 00:05:00
Exercise #3 – Data Loading and Analysis 00:01:00
Data Cleaning and Transformation
Overview 00:02:00
Removing Columns or Rows 00:04:00
Removing Duplicate Rows 00:09:00
Renaming Column Labels 00:04:00
Dropping Missing Values 00:08:00
Filling-in Missing Values 00:04:00
Creating Dummy Variables 00:08:00
Exporting Data into Files 00:04:00
Exercise #4 – Data Cleaning and Transformation 00:01:00
Course Summary
let’s recap 00:03:00

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