Conditions Apply

Machine Learning with Python Training in London, 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 (Weekday)

Apr 06 - May 08 05:00 PM - 07:00 PM ( BST )

GBP 2049

GBP 549

Online Classroom (Weekday)

Apr 13 - May 15 08:00 AM - 10:00 AM ( 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 London, UK

Machine Learning equips machines to automatically learn and improve from experience without any explicit programming. The systems are trained to access data and learn by themselves. The process starts with observing data to look for patterns, gather insights, and make future predictions. The main aim of machine learning is to help computers learn without any human assistance or intervention and act accordingly. The major algorithms used in Machine learning belong to one of the following categories:

  • Supervised Machine Learning Algorithms

In these types of algorithms, past, labeled data is used to get the information. This information is then applied on the new data to predict future events. The dataset that is fed to the system is used to train the machine learning model with the help of ML algorithms. Once the model has been trained, results for new input can be received.

  • Unsupervised Machine Learning Algorithms

These form of algorithms use unclassified and unlabeled data to train the model. These systems are built in such a way that can infer a function and decipher a hidden structure in the unlabeled data. The algorithms cannot get the exact results and draws inferences from the data for identifying and describing hidden patterns in the data.

 The field of Machine learning involves accessing data, analyzing it and using it to train the ML models. These models help in getting the best possible solution to a problem. Here are the reasons why Machine Learning has become an integral part of our society:

  • It's easy and it works

It is a fact that machines work faster and more efficiently than humans. This helps in reducing the human effort so that they can focus on other aspects of the application that require attention.

  • Being used in a wide range of applications today

Many real-world applications are powered by Machine Learning. Companies in London are using it to improve their efficiency. Domains like banking, transport, finance, healthcare, etc. have found usage in the field of machine learning.

UK saw a record 10,016 startups launching in 2017 and is still growing in spite of Brexit. With more than 100 AI startups, the Artificial Intelligence market is booming in London. Data has become an integral and irreplaceable part of these companies. It is now used for data-driven decision making that has helped several organizations in improving their businesses. Naturally, machine learning professionals are quite in demand right now. Here are some of the top reasons why you should start learning Machine Learning in London:

  1. Better job opportunities: Since the field has expanded to nearly every industry, there are better and more career opportunities. Currently, there are 1504 open jobs for Artificial intelligence in London.
  2. Better pay: The demand for qualified and experienced machine learning engineers is far more than the available professionals. Companies are willing to pay a hefty salary to machine learning engineers. In London, the average income of a machine learning engineer is £67,760 per year.
  3. High demand for ML skills: There is a huge gap between the supply and demand of machine learning engineers. With more and more companies beginning to incorporate ML, this demand is only going to increase in London.
  4. Usage in different industries: Almost every company in London is dealing with data. And where there is data, there is machine learning. Analyzing the data has become important to help the companies compete against other companies.

To learn machine learning, it is important that you stay motivated and keep on practicing. Hands-on experience will not only improve your skills but also help you retain the concepts for a longer time. Working on projects will help you build your profile. Here are a few tips that will help you learn Machine Learning:

  • Structural Plan: The first step is to create a plan that lists all the topics that will be covered in a detailed and structured way.
  • Prerequisite: Select a programming language that you are comfortable working with. You also need to revise your statistics and mathematics skills.
  • Learning: The third step is to dive into the learning process using the structured plan that you wrote. There are tons of books and guides available online that will help you learn the concepts of machine learning. There are also various boot camps in London offering project-based learning that prepare students for careers in AI.
  • Implementation: The last step is to practice. You can create your own projects using the algorithms that you've learnt. You can easily find datasets online that will help you implement your skills. You can also try participating in online competitions like Kaggle.

As an absolute beginner, you need to follow the below-mentioned steps to get started with Machine Learning:

  • Adjust your mindset: Machine learning is a tough field and it can be easy to get demotivated. You always need to remind yourself why you are doing this. 
  • Pick a process that suits you best: The next step is to create a systematic and structured process that will help you find a solution to your problem.
  • Pick a tool: There are several tools that can be used for implementing Machine Learning algorithms. While choosing one, you need to make sure you are comfortable with it. Here are a few examples:
    • Beginners - Weka Workbench
    • Intermediate level learners - Python Ecosystem
    • Advanced level learners - R Platform
  • Practice on Datasets: Search the web for datasets that you can use for practicing your ML skills. You should use small and installed memory datasets. Also, make sure that these are real-world datasets.
  • Build your own portfolio: The last step is to create projects in order to demonstrate your skills in the portfolio.

There are many universities in London offering Machine Learning courses, such as King's College London, University of London, etc. The key technical skills sets required to learn Machine Learning and become a Machine Learning engineer include:

  1. Programming languages: This is one of the most basic skill sets required to learn ML. You should be well-versed with languages like Python, Java, Scala, etc. You need this to implement the concepts of machine learning like processing and analyzing data.
  2. Database skills: Knowledge of SQL and relational database is as important as knowledge of programming. Real-world ML projects deal with huge databases and you need to have database skills for accessing and communicating with the available database.
  3. Machine Learning visualization tools: Data visualization is the presentation of data in a graphical format.
  4. Knowledge of Machine learning frameworks: Frameworks like R, Apache Spark, TensorFlow, ScalaNLP, etc. are used to analyze the data. The ML algorithms are fed into the framework to process the available data.
  5. Mathematical skills: Concepts of mathematics are used to process and analyze the data and create ML algorithms. Make yourself familiar with topics like calculus, Bayesian modeling, graph theory, probability, statistics, regression, linear algebra, hypothesis testing, etc.

Here are the steps that you need to follow for executing a successful Machine Learning project using Python:

  1. Gathering data: Collect the data that you will need for the analysis. It is essential that the data is of good quality as you will be implementing your machine learning skills on it.
  2. Cleaning and preparing data: The next step involves cleaning the data. Most of the data that is generated is unstructured data that cannot be used in the ML model. For this, you need to remove unnecessary data. After this, you have to convert the data into a form that can be read by the ML model using feature engineering. Next, the data is divided into two subsets- training and testing data.
  3. Visualize the data: The next step is visualizing the data. This will help you in finding a correlation among the data. 
  4. Choosing the correct model: Next, you need to select the right model and algorithm for your problem. The performance of your algorithm will depend on the quality of the data and the type of ML model.
  5. Train and test: This step involves injecting the prepared data into the ML model to train it. Next, the testing data is used to determine the accuracy of the model after it has been trained.
  6. Adjust parameters: The last step is to adjust the parameters of the model to make the ML model more accurate.

Here is how you can learn the top essential ML algorithms:

  1. List the various Machine Learning algorithms: The first step is creating a list of algorithms you will be studying. List them according to their type and classes.
  2. Apply the Machine Learning algorithms that you listed down: Focus on implementing machine learning algorithms on datasets. Apply algorithms like Support Vector Machines, Decision trees, etc. on different problems.
  3. Describe these Machine Learning algorithms: Write a description of the machine learning algorithms. This will help you get a better understanding of them.
  4. Implement Machine Learning Algorithms: The next step is using these machine learning algorithms in a real-world project. This will not only help you practice your implementation skills but will also build your portfolio.
  5. Experiment on Machine Learning Algorithms: Play with the algorithms. Get an understanding of the variables and functions involved in the algorithm. This will help you in customization of the algorithm according to your needs.

Machine Learning Algorithms

The most essential Machine Learning algorithm for beginners is K-Nearest Neighbors algorithm. It will help you get started in Machine Learning field as it is uncomplicated. The problem is predicting the data point’s class from a multiclass dataset. Here is how it works:

  • A number will be pre-defined and stored as ‘k’. This defines the number of training samples that are close to the data point.
  • The new data point is assigned a label that will be defined by and assigned to the neighbors.
  • The number of neighbors to be determined will be fixed and user-defined.
  • Then the density of the neighboring data points is used for identifying the samples and classifying them under a fixed radius (Euclidean distance). This is known as radius based classification.
  • Classification is performed after the vote is conducted among the unknown sample’s neighbors.

Whether you should learn algorithms depend on what you intend to do with it.

  • If you plan to just use the algorithm, you don’t need to study them. There are many online and offline courses in London that teach you Machine Learning algorithms.
  • If you are planning to innovate, you need to have a basic knowledge of ML algorithms. For this, you need to know the accuracy, complexity, cost and time involved in the algorithm. Once you have a complete understanding of the algorithm, you will be able to experiment with the concepts of Machine learning.

The top different types of Machine Learning algorithms include:

  • Supervised Learning: This involves using the historical data for understanding the mapping functions of input variables to output variables. The following algorithms use the supervised learning method:
    • Linear Regression
    • Logistic Regression
    • Classification and Regression Trees (CART)
    • Naïve Bayes
    • K-Nearest Neighbors
  • Unsupervised Learning: This method involves analyzing the given dataset for revealing possible associations and clusters. In this, the output variables are not provided. The algorithms that use unsupervised learning method are:
    • Apriori
    • K-Means
    • Principal Component Analysis (PCA)
  • Ensemble Learning: This method of learning involves using each learner’s result and combining them to get a correct representation of the outcome. The following algorithms follow the ensemble learning method.
    • Bagging
    • Boosting

According to the above-mentioned criteria, k-nearest neighbor algorithm is the simplest ML algorithms. Here is why:

  • It is the simplest supervised learning algorithm that can be used for regression and classification.
  • Labeled data is used during the training phase.
  • It is used for the following simple, real-world problems
    • Identifying the number of the vehicle plate
    • Searching the documents of the same topic
    • Detecting credit card usage pattern

There are a number of machine learning algorithms. It is very important that you select on the basis of the model and tools you are using, as the algorithm is the backbone of your project. Here is how you can do it:

  • Understanding your data: You need to understand your data before selecting the algorithm you will be using. Try the below-mentioned steps:
    • Visualize the data using graphs and charts
    • Identify the relationships present in the data
    • Clean the data
    • Prepare your data for injection into the model through feature engineering.
  • Get the intuition about the task: Understand what the aim of the task is. This will help you determine what type of learning method you will be using. Here are the types of learning methods:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: You can’t just select the best tool and the fastest algorithm. You need to understand the constraints with which you will be working as the best tools and algorithms will require high-end machines with high data storage and manipulation resources. Here are the 3 constraints you need to look for
    • Time 
    • Hardware 
    • Data storage
  • Find available algorithms: Select the algorithm that fits the above mentioned requirements and constraints.

Here is how you can practically design and implement a Machine Learning Algorithm:

  • Select a programming language: You need to select a programming language that you are familiar with and that can manipulate the standard libraries and APIs during the implementation process.
  • Select the algorithm that you wish to implement: Next, you need to select the algorithm, the classes you will be using, its description and the special implementation that you are planning to do.
  • Select the problem you wish to work upon: Next, select the problem dataset on which you will be implementing your algorithm on. You will be using this dataset for testing so you need to validate the efficiency of the algorithm and its implementation.
  • Research the algorithm that you wish to implement: Gather as much information as you can on the selected algorithm. The different implementation, outlooks, and description will help you get a clear perspective of the algorithm.
  • Undertake unit testing: In this last step, you will be developing and running every function’s unit test. Consider this as the test-driven development aspect of the algorithm in the initial phases of development.

You need to have an understanding of all the concepts of machine learning to become a machine learning engineer. However, there are some topics that are more commonly used and thus, you should master:

  • Decision Trees: It is a supervised learning algorithm used for classification problem. In this, you have to select the features and then use the conditions for splitting. It also involves determining the condition for splitting or ending iteration. It has the following advantages:
    • Easy to understand, implement, visualize, and interpret
    • Can perform feature selection and variable screening
    • Handles categorical and numerical data as well as problems requiring multiple outputs
    • Not affected by parameter’s non-linear relationship
  • Support Vector Machines:  This algorithm has a high accuracy rate in the classification problem. It has the following benefits:
    • Can be used for regression problems as well
    • Guarantees optimal solution
    • Can be used for linearly and nonlinearly separable data
    • Makes feature mapping easy through the ‘Kernel Trick’
  • Naive Bayes: This algorithm assumes that all the parameters are independent of each other. The advantages of using this classification techniques include:
    • Performs a bunch of counts that make it very easy
    • Converges quickly
    • Highly scalable
    • Requires less training data
  • Random Forest algorithm: This algorithm randomizes the input while creating the forest of decision trees. In this, the system doesn’t identify the input data’s pattern based on its order. Here are its advantages:
    • Can be used for classification and regression
    • Hyperparameters are easy to use
    • Produces good prediction result

Machine Learning Engineer Salary in London, UK

The median salary of a Machine Learning Engineer in London is £50,000/yr. The range differs from £30,000 to as high as £90,000

The average salary of a machine learning engineer in London compared with Manchester is £50,000/yr. whereas, in Manchester, it’s £45,000/yr.

According to a recent study, ML patents have seen a massive growth of about 344% in the last 3 years. Most of these ML patents were under huge tech companies like Microsoft and Facebook who also have a base in London and are constantly looking to upgrade themselves. Moreover, London is home to several top British tech companies who are aware of the potential of Machine learning. They all are in need of skilled engineers who can use their abilities to use machine learning to produce the best results. So it won’t be an understatement to say that Machine learning engineers are in high demand in London.

Being one of the most in-demand jobs in the present scenario, one can expect these amazing benefits that a machine learning engineering job can offer - 

  • High payout - One of the biggest reasons behind its demand among engineers is because of the high package it offers which is completely justifiable as you need to be highly trained to be a skilled Machine Learning Engineer. 
  • Acknowledgement - If you’re a machine learning engineer, you probably have either a degree of either PhD or masters which itself is a proof of the in-depth knowledge one might have regarding this field. Organizations often hire such an individual to seek their opinion or share some wisdom. This not only is a way of secondary income but a mark of respect and acknowledgement that the industry offers.

You know there is more than just a high salary when a job expands at a massive rate of 344% in just 3 years. This expansion is a result of the massive demand for ML engineers. Following are the advantages that an engineer can enjoy apart from just a high salary -

  • Career Stability
  • Growth in Job
  • Unexplored paths
  • Large bonus

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

  • Tesco
  • NICE
  • Institute of Microelectronics
  • VIAVI Solutions
  • Microsoft
  • J.P. Morgan
  • Facebook

Machine Learning Conference in London, UK

S.NoConference NameDateVenue

Machine Learning in Legal Profession

June 19, 2019 - June 21, 2019

Marriott Hotel Canary Wharf, London, United Kingdom


O’Reilly Artificial Intelligence Conference

October 14, 2019 - October 17 2019

Hilton London Metropole, Central London, London, United Kingdom


Minds Mastering Machines [M3]

September 30, 2019 - October 2, 2019

QEII Conference Centre, London

  1. Machine Learning in Legal Profession, London
    1. About the conference: The workshop is aimed to guide the participants to make the best use of technology and further their law firms in a stiff and competitive market. The conference will help the participants prepare for the changes brought in by Machine Learning advancements in the legal sphere. 
    2. Event Date: June 19, 2019 - June 20, 2019
    3. Venue: Marriott Hotel Canary Wharf, London, United Kingdom
    4. Days of Program: 2
    5. Timings: June 19 2019 8:30 AM - June 21 2019 3:00 PM
    6. Purpose: The agenda of the conference is to transform the legal field with the use of technology. Machine Learning will affect the legal system, unlike any other technology. The purpose of the conference is to discover the developments and technologies used in Machine Learning and utilizing this knowledge to its full capacity. 
    7. How many speakers: 19
    8. Speakers and Profile:
      • Oriol Vinyals (Research Scientist, DeepMind)
      • Edward Greffenstette (Research Scientist, Facebook AI Research)
      • Katja Hofmann (Senior Researcher, Microsoft Research)
      • Shreyansh Daftry (Robotics Technologist, NASA Jet Propulsion Laboratory)
      • Hado van Hasselt (Senior Staff Research Scientist, DeepMind)
      • Ali Eslami (Staff Research Scientist, DeepMind)
      • Pierre-Yves Oudeyer (Research Director, Inria)
      • Jakob Uszkoreit (Senior Staff Software Engineer, Google Brain)
      • Trevor Back (Project Manager/Research Lead, DeepMind Health)
      • Li Erran Li (Chief Scientist/Adjunct Professor, University)
      • Richard Turner (Reader in ML, University of Cambridge)
      • Sergei Bobrovskyi (Data Scientist, Airbus)
      • Ingmar Posner (Associate Professor in Engineering Science, University of Oxford)
      • Jens Kober (Associate Professor, TU Delft)
      • Vishal Motwani (Member of Technical Staff,, Salesforce)
      • Lillian Li (Investor, Eight Roads Ventures)
      • Jon McLoone (Director of Technical Services, Communication and Strategy, Wolfram Research Europe)
      • Gerben Oostra (Machine Learning Engineer, BigData Republic)
      • Ryan den Rooijen (Global Director of Data Sciences, Dyson)
    9. Registration cost:
      • Early Bird Pass - £495.83 + 20% VAT
      • Early Bird Pass Plus - £745.83 + 20% VAT
      • Startup Pass - £329.17 + 20% VAT
      • Startup Pass Plus - £579.17 + 20% VAT
      • Student/ Academic Pass - £245.83 + 20% VAT
      • Student/ Academic Pass Plus - £495.83 + 20% VAT
    10. Who are the major sponsors: 
      • Nervana
      • Qualcomm
      • Graphcore
      • Accenture
      • Nvidia 
  2. Artificial Intelligence Conference, London
    1. About the conference: With the world of Artificial Intelligence taking over, the conference promises healthy discussion on emerging technologies, development and profit.  
    2. Event Date: October 14, 2019 - October 17, 2019
    3. Venue: Hilton London Metropole, Central London, London, United Kingdom
    4. Days of Program: 3
    5. Purpose: The purpose of this conference is to create successful and substantial Artificial Intelligence plans, learn what is needed to apply the theoretical knowledge of AI to practical experiments and to discover the latest tools and technologies used in Artificial Intelligence.  
    6. Speakers and Profiles:
      • Sridhar Alla (Blue Whale)
      • Alasdair Allan (Babilim Light Industries)
      • Krishna Anumalasetty (Microsoft)
      • Sacha Arnoud (Waymo)
      • Zahra Ashktorab (IBM Research)
      • Bahman Bahmani (Rakuten)
      • Dylan Bargteil (The Data Incubator)
      • Antje Barth (MapR)
      • Martin Benson (Jaywing)
      • Aashish Bhateja (Microsoft)
      • Rajib Biswas (Ericsson)
      • Paris Buttfield-Addison (Secret Lab)
      • Douglas Calegari (Travelers)
      • Roger Chen (Computable)
      • Ira Cohen (Anodot)
      • Robert Crowe (Google)
      • Alexis Crowell Helzer (Intel)
      • Tim Daines (QuantumBlack)
      • Danielle Dean (Microsoft)
      • Danielle Deibler (Marvelous, Inc)
      • Jim Dowling (Logical Clocks)
      • Casey Dugan (IBM Research)
      • Ted Dunning (MapR)
      • Daniel First (QuantumBlack)
      • James Fletcher (GRAKN.AI)
      • Ariadna Font Llitjós (IBM Watson, Data and AI)
      • Michael Friedrich (Adobe)
      • Siddha Ganju (Nvidia)
      • Biraja Ghoshal (Tata Consultancy Service)
      • Martin Goodson (Evolution AI)
      • Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
      • Adam Grzywaczewski (NVIDIA)
      • Rebecca Gu (Baringa Partners LLP)
      • Charlotte Han (NVIDIA)
      • Kim Hazelwood (Facebook)
      • Ana Hocevar (The Data Incubator)
      • Adithya Hrushikesh (Vodafone )
      • Congrui Huang (Microsoft)
      • Ihab Ilyas (University of Waterloo | Tamr)
      • Alex Ingerman (Google)
      • Jewel James (GO-JEK)
      • Katharine Jarmul (KIProtect)
      • Jeff Jonas (Senzing)
      • Ahmed Kamal (Careem)
      • Manas Ranjan Kar (Episource)
      • Meher Kasam (Square)
      • Ujwal Kayande (Melbourne Business School)
      • Arun Kejariwal (Independent)
      • Ganesh Kesari (Gramener Inc)
      • Anastasia Kouvela (A.T. Kearney )
      • Akshay Kulkarni (Publicis Sapient)
      • Holger Kyas (University of Applied Sciences)
    7. Registration cost: 
      • Gold Pass - £895 + 20% VAT
      • Silver Pass - £745 + 20% VAT
      • Bronze Pass - £575 + 20% VAT
    8. Who are the major sponsors
      • IBM Watson
      • Dell Technologies
      • AXA
  3. Minds Mastering Machines [M3], London
    1. About the conference: The conference brings together experts in Machine Learning, Artificial Intelligence and Data Science. The conference is being organized by keeping in mind the needs of architects, developers and CIOs.
    2. Event Date: September 30, 2019 - October 2, 2019
    3. Venue: QEII Conference Centre, London, United Kingdom
    4. Days of Program: 3
    5. Timing: September 30 2019 10:15 AM - October 2 2019 5 PM
    6. Purpose: The aim of the conference is to show the participants how to use the tools and technologies of Artificial Intelligence and Machine Learning. The focus of the conference will also be on AI and ML tools and frameworks, technological and ethical pitfalls, implications of AI and ML, data science and robots and machine learning.
    7. Registration Cost:  
      • Blind Bird - £500 + £100 VAT
      • Early Bird - £400 + £80 VAT
      • Early Bird - £665 + £133 VAT
    8. Speakers: 28
    9. Speakers and Profiles:
      • Sebastian Riedel (Facebook AI Research)
      • Dr Lorien Pratt (Quantellia LLC)
      • Kate Kilgour (University of Dundee)
      • Chris Parsons (IBM)
      • Rebecca Gu (Baringa Partners)
      • Christian Winker (Datanizing)
      • Fabian Bormann (IAV)
      • Eleonore Mayola 
      • Fritz Ulli Pieper (Taylor Wessing)
      • Tamsin Crossland (Icon Solutions)
      • Jean David Behlow (Taylor Wessing)
      • Dr Janet Bastiman (StoryStream)
      • David Blumenthal Barby (Babbel)
      • Oliver Zeigermann
      • Daniel Geater (Quatilest)
      • Chris Wallas (Cloudera Fast Forward Labs)
      • Justina Petraityte (Rasa)
      • Richard Cassidy (Exabeam)
      • John Buyers (Osborne Clark)
      • Lars Gregori (SAP CX)
      • Terry McCann (Advancing Analytics)
      • Rupert Thomas (TTP Plc)
      • Sara Bertman (IAV)
      • David Tyler (Outlier Technology Limited)
      • Gerhard Hausmann (Barmenia Krankenversicherung)
      • Daniel Skantze (Peltarion)
      • James Frost (Quorum)
      • Christian Kniep (Amazon Web Services)
S.NoConference NameDateVenue
1.Strata Data ConferenceMay 22, 2017 – May 25, 2017Excel London
2.Open Data Science ConferenceOctober 12, 2017 – October 14, 2017Hotel Novotel London West, 1 Shortlands, London
  1.  Strata Data Conference, London
    1. About the conference: One of the largest data conferences series in the world, the focus of the conference is aimed at shaping important decisions across various disciplines. 
    2. Event Date: 22-23 May 2017 (Training) and 23-25 May 2017 (Conference and Tutorials)
    3. Venue: Excel London
    4. Days of Program: 4
    5. Purpose: The purpose of the conference was to share best practices, data case studies, and analytic approaches around Big Data, Cloud, stream processing, sensors, IoT, analytics, data engineering, data science, machine learning and advanced analytics.
    6. Speakers - 212
    7. Who were the major sponsors: 
      • Cloudera
      • O’Reilly
      • IBM
      • Intel
      • Dell EMC
      • Google Cloud
  2. Open Data Science Conference, London
    1. About the conference: The conference aimed at bringing together innovative ideas and people in the world of Data Science to discuss about the core practices used in the field.
    2. Event Date: 12-14 October, 2017
    3. Venue: Hotel Novotel London West, 1 Shortlands, London 
    4. Days of Program: 3
    5. Purpose: The purpose of the conference was to demonstrate the participants to the whole range of libraries, tools, apps, and notebooks related to Data Science. All the hot topics like deep learning, data visualization, and quantitative finance were discussed.
    6. Speakers: 75
    7. Registration cost: £219
    8. Who were the major sponsors:
      1. DataRobot
      2. Microsoft
      3. Intel
      4. CrowdFlower
      5. METIS

Machine Learning Engineer Jobs in London, UK

As a Machine Learning Engineer, you will be responsible for the following:

  • Building data and model pipelines
  • Designing and developing machine learning and deep learning systems
  • Executing machine learning algorithms
  • Operating experiments and tests
  • Extending existing ML libraries and frameworks

London is a leading tech startup hub in Europe and is home to between 4,000 and 5,000 active tech startups, including Truphone, Deliveroo, Improbable, Farfetch, etc. According to a study revealed by Tech Nation, the UK’s digital tech sector is worth nearly £184bn. AI market rocketed in London after Google acquired DeepMind for a reported £400 million in 2014. Due to the growing importance of data, more employers than ever are looking to hire machine learning engineers. Currently, there are 3000 Machine Learning Jobs in London available. 

Here is a list of companies that are hiring Machine Learning engineers in London:

  1. AI-Adam
  2. Apple
  3. Twitter
  4. Interdigital Comm Corp
  5. Babylon Health

If you want to network with fellow machine learning engineers in London, you can try one of the following professional groups:

  1. London Women in Machine Learning & Data Science
  2. Machine Learning in Finance
  3. Machine Learning at Mimecast London – Meetup group
  4. Machine Learning in Education
  5. Machine Learning UK

The most in-demand ML job roles in 2019 are:

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

Networking with other machine learning engineers is very important as it helps you in getting referrals, which is the primary interview source in the IT sector. Here is how you can network with other Machine Learning Engineers in London:

  • Online platforms like LinkedIn
  • Machine Learning conferences and tech talks
  • Social gatherings like meetups

Machine Learning with Python London, UK

To get started with using Python for mastering machine learning, you need to follow these steps:

  1. The first step is to stay motivated that you can learn Machine Learning.
  2. Download the SciPy kit and its packages and install it on your system.
  3. Familiarize yourself with all the functions.
  4. Load the dataset.
  5. Get an understanding of the dataset’s structure and workings through statistical summaries and data visualization.
  6. Start practicing on the dataset.
  7. Start building projects. Begin with an easy one and gradually move towards more complicated projects.

Here are the most important Python libraries used in implementation of machine learning with Python:

  • Scikit-learn: This library is used for data mining, data analysis and data science.
  • Numpy: This library uses N-dimensional arrays for providing high-performance.
  • Pandas: It uses high-level data structures for data preparation and extraction.
  • Matplotlib: This library is used for plotting 2D graphs and charts for data visualization.
  • TensorFlow: It is used to train, setup, and deploy artificial neural networks. You should use this library to implement deep learning concepts in your project.

If you are a beginner who wants to learn Python programming language, you can try the following 6 tips:

  • Consistency is Key: To learn Python, you need to practice every day.
  • Write it out: From the start, write everything that you learn. It will help in long-term memory retention. Also, it is a good habit as a developer to write everything down before implementing it. It helps you get a clear perspective of what you are doing and why you are doing it.
  • Go interactive: Try the interactive Python shell for practicing. You will be able to get a better understanding of Python’s data structures like strings, lists, dictionaries, etc. To initialize the Python shell, fire up the terminal, type python in the command line and hit enter.
  • Assume the role of a Bug Bounty Hunter: Solve each bug that you get while practicing. This will help you understand the concepts clearly and improve your implementation skills.
  • Surround yourself with other people who are learning: Socialize with people who are working in Python. Not only will it help you in getting useful tips and tricks, but you will also stay motivated to learn Python. Attend Machine Learning conferences in London to connect with like-minded people. 
  • Opt for Pair programming: Try pair programming method in which two programmers work together. One is the driver and the other is navigator. Driver is the one who writes the code while navigator is the one who makes sure that the code is correct and provides feedback.

The best Python libraries essential for Machine Learning in 2019 are:

  • Scikit-learn: It is used for mining and analyzing data.
  • SciPy: This library contains packages that are used for manipulating concepts of mathematics and engineering.
  • Numpy: It uses matrix and vector operations for providing efficiency.
  • Keras: This library is used for handling neural networks.
  • TensorFlow: This library is used for training, setting up, and deploying artificial neural networks using multi-layered nodes.
  • Pandas: It performs data preparation and extraction using high-level data structures.
  • Matplotlib: This library plots 2D graphs to visualize data.
  • Pytorch: It is used for dealing with Natural Language Processing.

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Computer Systems Analyst.
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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.

Have More Questions?

Machine Learning with Python Course in London

Machine Learning with Python Training in London

London's convergent blend of a modern and an ancient city is to be marveled at. It has people living and working in it from all around the globe and is the highest contributor to the UK?s economy. It has world heritage sites, museums, art galleries and mansions on one end of the spectrum and modern-day skyscrapers at the other end. London is a global city leading in education, commerce, finance, professional services, software development, technology, research and development and more. London, topping the global financial center's index generates about 30 percent of UK?s GDP. It is also home to over a hundred of Europe?s 500 largest companies and hosts the offices of 75 percent of the Fortune 500 companies along with numerous banks and companies. With high employment rates for the technically competent individuals in the software industry, Knowledge Hut's data analysis using Python course in London can give you that extra edge in your next interview.

About the Course

The machine learning using Python course in London is perfectly suited for professional software developers, programmers who want an in-depth course in the same. The online course is instructor-led and gets you into the design methodologies that deal with data analysis and machine learning scenarios using Python. The data analysis training using Python in London teaches one the usage of Python packages such as Pandas, Numpy and Scipy for data modeling and analysis and gives online training to ensure that you know how to use them. The e-learning environment makes it easy to learn according to your own schedule. Keeping Ahead of the Curve Python is fast growing to become one of the most commonly used high-level programming languages and is easier to learn when compared to C, C++, and Java. A certification in this is a highly sought-after credential in the software development industry. Having a machine learning training using Python keeps you ahead of your game in using the various Python packages.

KnowledgeHut Empowers you

This Machine Learning course in London by KnowledgeHut is comprehensive and the training is given by industry experts who outline the best techniques and practices of the tools in the software and give practical examples throughout the course. The price for the machine learning using Python course in London is reasonable and a worthy investment into a certification that is sure to propel your career to new heights. Taking up these online classes also help you prepare for an exam that can be taken up in the related field and perform outstandingly.