Skip to content
Machine Learning with Python cover image
Play overlay
Preview this course

Machine Learning with Python
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
24.1 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
  • Uplatz Certificate of Completion - Free

Overview

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

Machine Learning is one of the newest skills to emerge in the last decade, altering fields from consumer electronics and healthcare to retail. This has led to strong inquisitiveness about the industry among many students and working professionals. Machine learning has become an essential part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive investigation teams. If you are learning machine learning with Python, even as a beginner, this course will explain you ways to build your personal machine learning solutions. With all the data available today, machine learning applications are restricted only by your thoughts.

Machine learning is the kind of programming which gives computers the capability to automatically learn from data without being openly programmed. This means in other words that these programs change their performance by learning from data. For a software developer or a business analyst it is important to you might be curious about how machine learning can change the way you work and take your career to the next level. This machine learning tutorial will help you achieve your goals by becoming an expert in machine learning.

After successful completion of this course you will be able to:

  • Learn supervised and unsupervised learning algorithms
  • Learn linear regression to logistic regression to k-means clustering to random forest and other decision tree techniques
  • Using Pandas and NumPy to accomplish various data mining and data wrangling tasks to turn your data into useable training data
  • Learn to use scikit-learn
  • Comb over your available data and implement practical machine learning techniques
  • Learn to use computer science techniques
  • Build the foundation of artificial intelligence, big data, and predictive models
  • Build basic deep neural networks
  • Learning reinforcement and deep learning in machines

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Uplatz Certificate of Completion

Digital certificate - Included

Course Completion Certificate by Uplatz

Curriculum

1
section
31
lectures
24h 9m
total

    • 1: Introduction to Machine Learning Preview 40:26
    • 2: Types of Machine Learning - part 1 43:27
    • 3: Types of Machine Learning - part 2 49:33
    • 4: Components of Python ML Ecosystem - part 1 28:23
    • 5: Components of Python ML Ecosystem - part 2 39:00
    • 6: Components of Python ML Ecosystem - part 3 1:01:48
    • 7: Components of Python ML Ecosystem - part 4 13:13
    • 8: Regression Analysis (I) - part 1 38:11
    • 9: Regression Analysis (I) - part 2 1:08:45
    • 10: Regression Analysis (I) - part 3 33:20
    • 11: Regression Analysis (I) - part 4 48:09
    • 12: Regression Analysis (II) - part 1 29:28
    • 13: Regression Analysis (II) - part 2 56:31
    • 14: Regression Analysis (II) - part 3 1:01:16
    • 15: Classification (I) - part 1 47:35
    • 16: Classification (I) - part 2 1:07:06
    • 17: Classification (II) - part 1 35:54
    • 18: Classification (II) - part 2 41:42
    • 19: Classification (II) - part 3 44:03
    • 20: Classification (II) - part 4 35:03
    • 21: Clustering (I) - part 1 57:04
    • 22: Clustering (I) - part 2 57:32
    • 23: Clustering (II) - part 1 1:01:57
    • 24: Clustering (II) - part 2 45:13
    • 25: Association Rule Learning - part 1 1:11:02
    • 26: Association Rule Learning - part 2 1:16:49
    • 27: Recommender Systems - part 1 32:55
    • 28: Recommender Systems - part 2 1:00:19
    • 29: Recommender Systems - part 3 20:45
    • 30: Recommender Systems - part 4 1:17:16
    • 31: Machine Learning with Python - Interview Questions 05:00

Course media

Description

Machine Learning using Python - Course Syllabus

1. Introduction to Machine Learning

  • What is a Machine Learning?
  • Need for Machine Learning
  • Why & When to Make Machines Learn?
  • Challenges in Machines Learning
  • Application of Machine Learning

2. Types of Machine Learning

  • Supervised
  • Unsupervised
  • Reinforcement

3. Components of Python ML Eco system

  • Using Pre-packaged Python Distribution: Anaconda
  • Jupyter Notebook
  • NumPy
  • Pandas

4. Regression Analysis (I)

  • Regression Analysis
  • Linear Regression
  • Examples on Linear Regression
  • scikit-learn library to implement simple linear regression

5. Regression Analysis (II)

  • Multiple Linear Regression
  • Examples on Multiple Linear Regression
  • Polynomial Regression
  • Examples on Polynomial Regression

6. Classification (I)

  • What is Classification
  • Classification Terminologies in Machine Learning
  • Types of Learner in Classification
  • Logistic Regression
  • Example on Logistic Regression

7. Classification (II)

  • What is KNN?
  • How does the KNN algorithm work?
  • How do you decide the number of neighbors in KNN?
  • Implementation of KNN classifier
  • What is a Decision Tree?
  • Implementation of Decision Tree

8. Clustering (I)

  • What is Clustering?
  • Applications of Clustering
  • Clustering Algorithms
  • K-Means Clustering
  • How does K-Means Clustering work?

9. Clustering (II)

  • Hierarchical Clustering
  • Agglomerative Hierarchical clustering and how does it work
  • Working of Dendrogram in Hierarchical clustering
  • Implementation of Agglomerative Hierarchical Clustering

10. Association Rule Learning

  • Association Rule Learning
  • Apriori algorithm
  • Working of Apriori algorithm
  • Implementation of Apriori algorithm

11. Recommender Systems

  • Introduction to Recommender Systems
  • Content-based Filtering
  • How Content-based Filtering work
  • Collaborative Filtering
  • Implementation of Movie Recommender System

Who is this course for?

Everyone

Requirements

Passion to learn and succeed!

Career path

  • Machine Learning Engineer
  • Deep Learning Engineer
  • Data Scientist
  • Data Science Engineer
  • Python Programmer
  • Python Data Science Developer
  • Python Machine Learning Engineer
  • Machine Learning Scientist
  • Data Analyst
  • Data Strategist/Architect
  • Machine Learning Application Developer
  • Bigdata and ML Engineer
  • AI/ML Engineer - Python/Deep Learning
  • Staff Engineer - Machine Learning
  • BI/Visualization Developer

Questions and answers

Currently there are no Q&As for this course. Be the first to ask a question.

Ask a question

Reviews

Currently there are no reviews for this course. Be the first to leave a review.

Leave a review

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

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
View all
Similar subjects: Python, Machine Learning, Data science, Coding, IT
Related searches: Online courses

Interest free credit agreements provided by Zopa Bank Limited trading as DivideBuy are not regulated by the Financial Conduct Authority and do not fall under the jurisdiction of the Financial Ombudsman Service. Zopa Bank Limited trading as DivideBuy is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, and entered on the Financial Services Register (800542). Zopa Bank Limited (10627575) is incorporated in England & Wales and has its registered office at: 1st Floor, Cottons Centre, Tooley Street, London, SE1 2QG. VAT Number 281765280. DivideBuy's trading address is First Floor, Brunswick Court, Brunswick Street, Newcastle-under-Lyme, ST5 1HH. © Zopa Bank Limited 2026. All rights reserved.