Machine Learning with Python Training in Bristol, United Kingdom

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 350000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Machine Learning with Python skills

In this four-week course, you will dive into the basics of machine learning using python; a well-known programming language. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Machine Learning with Python?


Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire the complete machine learning course with Python skills and meet that need.

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Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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Machine Learning Course with Python Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  


  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  


  • Learning to implement statistical operations in Excel

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of functions and how to operate on files to read and write data in Python  
  • Understand how to use Pandas - a must have package for anyone attempting data analysis with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 


  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Applied Machine Learning in Python via real-life examples and the multiple ways in which it affects our society. You will learn: 

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling relates to Machine Learning 


  • Machine Learning Modelling Flow  
  • How to treat Data in ML  
  • Types of Machine Learning  
  • Performance Measures  
  • Bias-Variance Trade-Off  
  • Overfitting and Underfitting  

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp


  • Maxima and Minima  
  • Cost Function  
  • Learning Rate  
  • Optimization Techniques  

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp


  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study


  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers 
  • Predict if a patient is likely to get any chronic kidney disease based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  


  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study


  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 


  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study


  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 


  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study


  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python certification Course is one of the best machine learning with python courses. This course is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

Learn Machine Learning with Python through a curriculum designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of practical machine learning with python skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The Machine Learning with Python syllabus is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the learners through machine learning algorithms in python, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases, all in all making it the best machine learning with python course. 

Yes, our Machine Learning with Python certification course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

The complete Machine Learning Course with Python requires daily training hours. In addition to the training hours, we recommend spending about 2 hours every day, for the duration of the course.   

Machine Learning course with Python is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems 
  2. Software or Data Engineers interested in quantitative analysis with Python 
  3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this Machine Learning with Python certification course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the complete Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below - 

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your advanced Machine Learning with Python skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

Our introduction to Machine Learning with Python course will give you an opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

Learn Machine Learning with Python at KnowledgeHut which is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning with Python certification course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, python machine learning projects, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on and we will be happy to get back to you. 

The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.


Prerequisites for Machine Learning With Python

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

What you will learn in the Machine Learning with Python course

Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.

Fundamentals of Machine Learning

Learn Machine Learning with Python, including Supervised and Unsupervised Machine Learning.  

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .

Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis.

Skills you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

Transform Your Workforce

Harness the power of data to unlock business value

Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.  

  • Custom Training Solutions. 
  • Applied Learning.
  • Learn by doing approach.
  • Get in touch for customized corporate training programs.

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Learn Machine Learning

Learn Machine Learning in Bristol, UK

Machine learning involves helping the system to learn, understand, perform, and improve the solution to a given problem without any human intervention. This requires the usage of concepts of data science and artificial intelligence. The major algorithms of Machine Learning can be categorized into the following:

  • Supervised Machine Learning Algorithms: These involve using labeled data into the system for getting the past information and applying it to the new data. Here is how it is done:
    • The dataset is loaded into the system. It is the training data for the ML model.
    • This results in an algorithm that is used to create inferred function.
    • The algorithm is then used to make future predictions for the new inputs.
  • Unsupervised Machine Learning Algorithms: These algorithms use unlabelled, unorganized data to help train the model. They can’t provide the accurate result but they can infer a function by finding a hidden structured in the unstructured data.

Machine Learning helps the system learn without any human intervention. The right algorithm can help in evaluating and resolving a problem faster and better. Different domains have found applications of Machine Learning in their work. It helps them save money, time, and efforts. These domains include finance, transport, health care, government institute, banking, customer service, etc.

According to Tech Nation, Bristol is the most productive digital cluster in the UK, with more than 35,924 digital jobs and 225 start-ups. Machine Learning is primarily used in these companies to process large amounts of data quickly. From small startups to MNCs, almost every company has started shifting towards data-driven decision making. This has made ML a very lucrative field. Here are some top reasons to learn Machine Learning in Bristol:

  1. It reels in better job opportunities: 

Bristol is fast becoming known as a national and international digital hub. As the Machine learning field is expanding, opportunities for machine learning engineers are also increasing in Bristol.

  1. Machine Learning engineers earn a pretty penny: 

Since there are not enough qualified machine learning engineers, the ones who are available, are paid handsomely. In Bristol, the average income of a machine learning engineer is £46,605 per year.

  1. Demand for Machine Learning skills is only increasing: 

According to Indeed, machine learning engineer is the best job of 2019 due to growing demand and high salaries. There is a huge gap between the demand and the availability of professionals with Machine Learning skills in Bristol. The demand, as well as the pay for Machine Learning engineers, is only going to increase in the coming future. 

Here is how you can learn Machine Learning:

  • Structural Plan: The first step is to create a structured plan on the topics that you need to learn.
  • Prerequisite: You need to brush up your programming, mathematics, and statistical skills before you dive into Machine Learning.
  • Learning: Take the help of books, video tutorials, and courses that are available online for free and get started with learning.
  • Implementation: Start working on projects where you can implement the algorithms you are studying. You can also try to participate in online competitions or boot camps in Bristol. 

If you are an absolute beginner, here is what you need to do to get started in Machine Learning:

  • Adjust your mindset: The first step is to find out what motivates you to keep going. This will help you stay focused till you finish learning.
  • Pick a process that suits you best: The next step is to find a structured, systematic process that suits you.
  • Pick a tool: This involves picking up a tool you will be using for practice. Here are a few suggestions:
    • Beginners - Weka Workbench
    • Intermediate - Python Ecosystem
    • Advanced - R Platform
  • Practice on Datasets: To improve your machine learning skills, you need to practice on as many datasets as possible. These datasets must be small and real-world problems.
  • Build your own portfolio: For demonstrating your ML skills and building up your CV, you should start creating projects.

The tech talent of Bristol is waiting to be unleashed. Bristol is home to world-renowned universities, including The University of Bristol that graduates more than 5,000 software engineers and other tech related students each year. If you want to learn Machine Learning and become a Machine Learning Engineer, you must have the following technical skills:

  1. Programming languages: You need to have knowledge of programming languages like Python, Java, Scala, etc. and knowledge of data formats, data processing, etc, to implement the concepts of machine learning.
  2. Database skills: Since while working on an ML project, you will be dealing with databases, you need to have a thorough understanding of relational databases and SQL. Also, you should be familiar with converting the data into a format that can be read by the framework.
  3. Machine Learning visualization tools: You need to be familiar with the tools used in visualizing the data.
  4. Knowledge of Machine learning frameworks: Knowledge of frameworks like Apache Spark, ScalaNLP, TensorFlow, R, etc. is required to become a Machine Learning engineer.
  5. Mathematical skills: These are required to process the data and create the ML model. Knowledge of concepts like calculus, statistics, probability, linear algebra, graph theory, Bayesian modeling, etc. is a must.

Here is how you can learn the top essential Machine Learning Algorithms:

  1. List the various Machine Learning algorithms: Create a list of all the algorithms you wish to learn and categorize them as per their class and type.
  2. Apply the Machine Learning algorithms that you listed down: Use the real-world datasets to implement the algorithm. 
  3. Describe these Machine Learning algorithms: Write a short description of the algorithm. This will help you create a mini-encyclopedia of ML algorithms.
  4. Experiment on Machine Learning Algorithms: Play with datasets, variables, and functions to fully understand the algorithm. This will also help you in customizing the algorithm to suit your needs.

Machine Learning Algorithms

The most essential machine learning algorithm for beginners is the k nearest neighbor algorithm. Here is how the algorithm works:

  • A number ‘k’ is predefined that stores the number of training samples close to the new data point.
  • This new data point is assigned a label that is defined and assigned by the neighbors.
  • The K-nearest neighbor classifiers has a fixed number of neighbors
  • The density of the neighbor data points is measured by classifying the samples under a fixed radius. This method is known as radius based classification.
  • This algorithm following the non-generalizing ML method, remembers the training data.
  • Classification takes place after unknown sample’s neighbors take the vote.

If you just want to use the algorithms, you don’t need to have a thorough knowledge of the ML algorithms. However, if you are planning to use the concepts of machine learning to innovate or want to create a new algorithm, you need to have a basic knowledge of algorithms. To experiment with machine learning, you must have an understanding of how complex an algorithm is, how correct it is, the constraints involved and time taken by the algorithm, etc.

Below are the top different types of Machine Learning Algorithms:

  • Supervised Learning: In this method of learning, classified data from past is used to map the input variables to the output variables. Here is a list of algorithms that follow supervised learning method:
    • Linear Regression
    • Logistic Regression
    • Classification and Regression Trees (CART)
    • Naïve Bayes
    • K-Nearest Neighbor
  • Unsupervised Learning: In Unsupervised Learning method, only the input variables are provided and not the output ones. Possible clusters and associations are revealed after analyzing the underlying structures of the dataset. The following algorithms use this method:
    • Apriori
    • K-Means
    • Principle Component Analysis (PCA)
  • Ensemble Learning: This learning method involves using the result from each learner and combining them to get the better representation of the output. Here are a few algorithms that use Ensemble Learning:
    • Bagging
    • Boosting

The simplest Machine Learning algorithm for beginners is the k-nearest neighbor algorithm. Here are the reasons behind this:

  • The simplest supervised learning algorithm.
  • Used for regression and classification problems.
  • Uses similarity measure for classification.
  • Uses Labeled data for training.

To decide and choose the right Machine Learning algorithm to use for a specific problem statement, you need to follow these steps:

  • Understanding your data: Your data will decide what algorithm you will be selecting. Clean the data and use graphs and charts to visualize the data. The next step is to perform feature engineering to prepare the data for analysis.
  • Get the intuition about the task: Understand your task to determine the type of learning you will be using. There are 4 types of learning methods:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: For every project, there are a couple of restraints that you need to follow. Some of them are:
    • Data storage – It limits the amount of data you can use for training and testing the model.
    • Hardware – It is determined by the computational power of your machine.
    • Time – It determines the length of the training.

For practically designing and implementing a Machine Learning Algorithm using Python, you need to follow these steps:

  • Select the algorithm that you wish to implement: The first step is to select the algorithm that you will be implementing using Python. After this, you also need to select its classes, description, types, and special implementation.
  • Select the problem you wish to work upon: Select the problem that you want to work on. This includes implementing the algorithm and validating its efficiency.
  • Research the algorithm that you wish to implement: Go through different resources to get the implementation, description, and outlook of the algorithm. 
  • Undertake unit testing: This step involves developing and running tests on all the functions used in the algorithm.

Below are the most essential topics of Machine Learning one should study to master:

  • Decision Trees: This supervised learning algorithm is used for classification problems. It is simple and easy to interpret, visualize, and understand. Here are the benefits of using Decision trees:
    • Can be used for variable screening and feature selection
    • Is not affected by parameters’ non-linear relationship
    • Can analyze categorical and numerical data
    • Less effort required in preparing the data
  • Support Vector Machines: This method can be used for classification as well as regression problems. Thanks to its Kernel trick, feature mapping has become quite easy. It can be used for linear and non-linear data. 
  • Naive Bayes: It is a classification technique that is based on Bayes theorem, the only difference being that it considers that all the predictors are independent of each other. It quickly converges, requires less training data, and is highly scalable.
  • Random Forest algorithm: This supervised learning algorithm creates a forest of decision trees and randomizes the input so that no pattern is identified based on its order. It is used for classification as well as regression problems.

Machine Learning Engineer Salary in Bristol, UK

The median salary of a Machine Learning Engineer in Bristol is £41,000/yr. The range differs from £26,600 to as high as £63,100.

The average salary of a machine learning engineer in Bristol compared with London is £41,000/yr. whereas, in London, it’s £50,000/yr.

Companies like Facebook, Apple and Microsoft are the majority of patent holders and are highly interested in growing their reach. Bristol is considered as one of the most developed cities in England, and is also technologically advanced; all these factors prove that ML Engineers are in good demand and the numbers will only increase with time.

The job of Machine Learning engineers has risen up to be one of the most desired jobs in recent years. Following are the top benefits of having the most desired job - 

  • High Pay
  • Incentives and bonus
  • Acknowledgement
  • Possibilities
  • Scope to grow

Besides the higher paycheck, various advantages of being a Machine Learning Engineer consolidates - 

  • The ever-rising demand for ML engineers. It was reported that in a span of just 5 years, the demand for a skilled Machine learning engineer has grown more than 9 times of what it was. The slope is expected to rise even further due to the fact that Machine learning will play a very vital role in our future.
  • Better network opportunities. Machine learning is connected to Artificial Intelligence and Data science. All these are heavily linked to each other and these career roles are highly sought after.

Although there are quite many companies offering jobs to Machine Learning Engineers in Bristol, following are the prominent companies

  • Dyson
  • OnCorps
  • Apple
  • Jaguar
  • Microsoft
  • Facebook

Machine Learning Conference in Bristol, UK

S.NoConference nameDateVenue

Advancing analytics and AI in Bristol, North Somerset and South Gloucestershire

July 9th, 2019

City Hall, College Green, Bristol, BS1 5TR, United Kingdom


Anthropology + Technology Conference Bristol 2019

3rd October 2019

The watershed, 1 Canons Road, Harbourside, Bristol BS1 5TX, UK

  1. Advancing analytics and AI in Bristol, North Somerset and South Gloucestershire
    1. About the conference: The conference focused on data and analytics with relation to AI as a tool for analytics.
    2. Event Date: 9th July, 2019
    3. Venue: City Hall, College Green, Bristol, BS1 5TR, United Kingdom
    4. Days of Program: 1
    5. Timings: 14:00 -19:00 BST
    6. Purpose: To network with analyst colleagues, discuss local analytic projects and learn of new techniques and technologies.
    7. With whom can you Network in this Conference:
    8. Analysts
    9. Health care professionals 
    10. Registration cost: Free
    11. Who are the major sponsors:
    12. Bristol Health Partners
  2. Anthropology + Technology Conference Bristol 2019
    1. About the conference: Helping businesses to develop socially-responsible AI by conducting discussions.
    2. Event Date: 3rd October, 2019
    3. Venue: The watershed, 1 Canons Road, Harbourside, Bristol BS1 5TX, UK
    4. Days of Program: 1
    5. Purpose: To raise awareness of the importance of the collaboration between social scientists and technologists.
    6. How many speakers: 5
    7. Speakers & Profile:
      • Prof. Joanna Bryson: Reader (Associate Professor) Department of Computer Science, University of Bath.
      • Dr. Julien Cornebise: Director of Research and Head of London Office Element AI
      • Anna Kirah: Design Anthropologist 
      • Prof. Sarah Pink: Director, Emerging Technologies Research Lab, monash University, Australia 
      • Dr. Simon Roberts: Co-founder and partner Stripe partners 
    8. With whom can you Network in this Conference:
      • Technologists 
      • Anthropologists
      • Artificial intelligence professionals
      • Computer science professionals
      • Data science professionals 
      • Machine learning professionals 
      • Sociology professionals 
    9. Registration cost: 
      • Standard: 95£
      • Premium: 135£
      • Student: 50£
S.NoConference NameDateVenue

ICCCI 2018: 10th International Conference on Computational Collective Intelligence

September 05, 2018 - September 07, 2018

UWE Bristol Exhibition and Conference Centre of the University of the West of England - UWE Bristol

  1. ICCCI 2018: 10th International Conference on Computational Collective Intelligence, Bristol
    1. About the conference: The conference provided an international platform to researchers in the field of computer-based collective intelligence with special focus on Automated Reasoning, Data Integration, Data Mining and Machine Learning. 
    2. Event Date: September 05, 2018 - September 07, 2018
    3. Venue: UWE Bristol Exhibition and Conference Centre of The University of the West of England - UWE Bristol
    4. Days of Program: 3
    5. Purpose: The purpose of this conference was to provide a platform and discuss topics related to Data Processing and Machine Learning. 
    6. How many speakers: 4
    7. Speakers and Profile
      • Prof. Andrew Adamatzky from Department of Computer Science at the University of West of England, Bristol, UK 
      • Prof. Anthony Pipe from Bristol Robotics Laboratory, UK 
      • Dr. Tadeusz Szuba from the Department of Applied Computer Science, AGH University of Science and Technology, Poland
      • Dr. Jan Treur from Behavioural Informatics Group, Vrije Universiteit Amsterdam, The Netherlands
    8. Registration cost: 
      • Full Registration: £550 (Before 1 June); £600 (After 1 June)
      • Student Registration: £400 (before 1 June); £420 (After 1 June)
    9. Who are the major sponsors
      • The University of the West of England (UWE Bristol)
      • Wroclaw University of Science and Technology, Poland 
      • IEEE SMC Technical Committee on Computational Collective Intelligence

Machine Learning Engineer Jobs in Bristol, UK

From coding to deployment, testing and troubleshooting the issues, Machine learning engineers are responsible for multiple tasks. Some of the major responsibilities include, executing machine learning algorithms, creating machine learning and deep learning systems, and conducting tests and experiments.

Bristol is fast becoming known as a national and international digital hub. Oracle has introduced a new accelerator program to support startups in Bristol and it is home to some of the international tech companies including Amazon, Sony, Just Eat as well as supercomputer player Cray. Currently, there are 98 Artificial Intelligence jobs available in Bristol. Companies like Graphcore, Rovco, CPS Group UK, IO Associates, Searchability, Cookpad Ltd, etc. are looking for professionals to help them analyze their data.

Some of the companies hiring in Bristol are:

  • ApplyGateway Premium
  • TipTop Jobs
  • Opus RS
  • SR2

The top professional groups for Machine Learning Engineer in Bristol:

  • Bristol Machine Learning Meetup
  • Bristol Machine Learning Kitchen
  • PyData Bristol
  • IBM Code Bristol

Here are some of the jobs in the field of Machine Learning that are in demand in 2019:

  • Machine Learning Engineer
  • Cloud Architects
  • Data Mining Specialists
  • Cyber Security Analysts
  • Data Scientist
  • Data Architect

Here is how you can network with other Machine Learning Engineers in Bristol:

  • Machine Learning conferences
  • Social gatherings like meetups
  • Online platforms like LinkedIn

In Bristol, the average salary for a Machine Learning Engineer is £46,605 per year.,-Bristol-ENG  

Machine Learning with Python Bristol, UK

Here is how you can get started using Python for mastering Machine Learning:

  1. The first step is to determine your end game and stay focused on it.
  2. Next comes the installation of Python SciPy kit and its packages.
  3. Learn about the functions used in python.
  4. Download a dataset. Load it, understand its structure and how it works. You can do this through data visualization and statistical summaries.
  5. Practice your skills on as many datasets as you can.
  6. Start building projects.

If you want to implement Machine Learning with Python, you need to have an understanding of the following libraries:

  • Scikit-learn: It is used for data mining, data analysis, and data science.
  • Numpy: It offers high performance when used with N-dimensional arrays.
  • Pandas: It is used for extraction and preparation of data through high-level data structures.
  • Matplotlib: It helps in data visualization by plotting 2D graphs.
  • TensorFlow: It is used for implementation of deep learning.

If you want to execute a successful Machine Learning project using Python, you need to follow the below-mentioned steps:

  • Gathering data: The first step is to gather the relevant data for your project. Make sure that it is of good quality as it will impact the performance of your ML model.
  • Cleaning and preparing data: The gathered data will be in an unstructured form and inconsistent. You need to remove the unnecessary data and find the missing one. Then, using feature engineering, you need to convert the data into a format acceptable by the ML model. After this, you need to divide into two parts- training and testing data.
  • Visualize the data: Data visualization is required to help understand the data and relationships among the different variables. Also, it makes the data easy for the non-technical members of the team to understand.
  • Choosing the correct model: Next step is selecting the model and algorithm you will be using for analyzing your data.
  • Train and test: After this comes the training of the ML model using the training data. Once the training is finished, testing data is used to measure the accuracy of the model.
  • Adjust parameters: This step includes adjusting the parameters of the model to increase its accuracy.

If you are a beginner in programming and want to learn Python, here are 6 tips that you can use:

  1. Consistency is Key: Practice as much as you can. Practice coding for 30 minutes every day and then slowly increase the coding time.
  2. Write it out: Taking notes will help you retain things. It will also help you brush up on important topics while applying for a job or while working as a python developer.
  3. Go interactive!: You can use the interactive Python shell to practice strings, dictionaries, lists, etc. To do so, open a terminal, type ‘python’ and press enter.
  4. Assume the role of a Bug Bounty Hunter: As you start programming, you will come across several bugs. Solve them as they will improve your programming skills.
  5. Surround yourself with other people who are learning: Socializing with fellow Python developers will not only help you stay motivated to learn python but also will help you when you are stuck at a problem. Participate in meetups in Bristol to meet professionals.
  6. Opt for Pair programming: Pair programming is another efficient form of coding. In this, 2 developers are involved. The first one is the driver who writes the code. The second one is the navigator who guides the process and provides frameworks.

The following python libraries are essential for Machine Learning:

  • Scikit-learn: It is used for data analysis, data mining, and data science.
  • SciPy: This library is used in manipulation of concepts of engineering, mathematics, and statistics.
  • Numpy: This handles vector and matrix operations fast and efficiently.
  • Keras: This library can be used to deal with neural network.
  • TensorFlow: It uses multi-layered nodes setup, train, and deploy artificial neural networks quickly.
  • Pandas: This library extracts and prepares data with the help of high-level data structures
  • Matplotlib: It performs data visualization using 2D graphs.
  • Pytorch: It helps with Natural Language Processing.

What learners are saying

Shifa Al Kiyumi RTO Engineer
A useful course, I acquired knowledge about Python, Machine Learning Modeling Flow, Treating Data, Statistical Learning and other topics. I will use this training and even the recorded videos and materials from knowledge Hut for future projects.

Attended Machine Learning with Python workshop in July 2021

Amanda H Senior Front-End Developer

You can go from nothing to simply get a grip on the everything as you proceed to begin executing immediately. I know this from direct experience! 

Attended Full-Stack Development Bootcamp workshop in July 2021

Amanda H Senior Front-End Developer

You can go from nothing to simply get a grip on the everything as you proceed to begin executing immediately. I know this from direct experience! 

Attended Front-End Development Bootcamp workshop in June 2021

Emma Smith Full Stack Engineer

KnowledgeHut’s FSD Bootcamp helped me acquire all the skills I require. The learn-by-doing method helped me gain work-like experience and helped me work on various projects. 

Attended Full-Stack Development Bootcamp workshop in June 2021

Yancey Rosenkrantz Senior Network System Administrator

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

Godart Gomes casseres Junior Software Engineer

Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

Attended Agile and Scrum workshop in January 2020

Estelle Dowling Computer Network Architect.

I was impressed by the way the trainer explained advanced concepts so well with examples. Everything was well organized. The customer support was very interactive.

Attended Agile and Scrum workshop in February 2020

Anabel Bavaro Senior Engineer

The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

Attended Certified ScrumMaster (CSM)® workshop in August 2020

Machine Learning with Python Course in Bristol

A city known for its history as much as for its technological advancement, Bristol is on the cusp of a major transformation. Known for years as a centre for heavy industries, Bristol has managed to retain its rich culture and history even as it embraces modernism and all things techno savvy. There are plenty of sights and sounds to satisfy the art lover including its galleries, museums, art houses, murals, and iconic mansions and parks where one can spend some leisurely time away from the bustling city. Bristol with its world class universities, colleges and fast growing economy is a perfect place for students to learn and grow. You can choose among several of KnowledgeHut?s courses to start your career in Bristol. These courses are globally recognized and will get you off to a flying start. Courses include PRINCE2, PMP, PMI-ACP, CSM, CEH, Big Data, Hadoop, Python, Data Analysis, Android Development and much more. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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