Conditions Apply

Machine Learning with Python Training in Bristol, United Kingdom

Know how Statistical Modeling relates to Machine Learning

  • 50 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques

Online Classroom (Weekend)

Apr 04 - May 16 08:00 AM - 12:00 PM ( BST )

GBP 2049

GBP 549

Online Classroom (Weekend)

Apr 04 - May 30 09:30 AM - 12:30 PM ( BST )

GBP 2049

GBP 549

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to master this science and understand Machine Learning algorithms, which include Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from KnowledgeHut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

IBM’s Deep Blue beat the world champion in a game of chess.

Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

IBM’s Watson beat its human competitors at Jeopardy.

Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on past search patterns of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as to identify clients with high-risk profiles.

  1. Government:

Machine learning is finding varied uses in running government initiatives. It helps in detecting fraud and minimizes identity theft. It’s also used to filter and identify citizen data.

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn


For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having 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 in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

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

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling 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 & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

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

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using 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 depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 


Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

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.

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The Course

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Machine Learning experts who have years of industry experience.

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

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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.