Python has become a powerful language of data science and is now commonly used as the leading programming language for predictive analytics and artificial intelligence. During this hands-on “Machine Learning with Python” training course, you will learn to utilise the most cutting edge Python libraries for clustering, customer segmentation, predictive analytics and machine learning on the real-world data.
The course explores practical applications of the most frequently used machine learning approaches such a Multiple Linear, Polynomial (non-linear) and Logistic Regressions, k-Means and Hierarchical Clustering, k-Nearest Neighbours, Naive Bayes, Decision Trees and ensemble algorithms e.g. Random Forests, Adaptive Boosting or Extra Gradient Boosting approaches using Python’s major scientific libraries such as NumPy, pandas, SciPy as well as more specialised, statistical and machine learning oriented packages e.g. scikit-learn, statsmodels, and h2o.
Apart from this, you will learn to evaluate the predictive models based on the obtained metrics such as sensitivity, specificity, F-score, Kappa etc., and optimise the accuracy and efficiency of these models using various methods of cross-validation, grid-search and performance boosting. Please note this training course doesn’t include Neural Networks and Deep Learning approaches.
Who is this course for?
This tutor-led online course is suitable for data scientists, researchers, data analysts, developers and engineers, who are currently using Python language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit. This course is also recommended for undergraduate and postgraduate students in social sciences, computer science, data science, economy, finance/banking, public health and medical science, and all other fields which implement machine and statistical learning methods.
This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners,
In total, this course includes 15 hours of live teaching which equates to a 3-day classroom-based training course with exclusion of tea/coffee and lunch breaks,
Exercises and tasks completed by you between the live tutorial sessions will help you in better material retention and will enhance your learning progress,
Small group size (up to 15 learners) allows easy interaction and stimulating environment for successful learning,
Email supervision and support provided during the course period maximises learning outcomes and improves your learning experience,
You will have access to additional self-paced online learning materials e.g. tutorial videos, exercises and quizzes, Python code scripts used during tutorials, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
You will receive a course attendance certificate which can be upgraded to a course completion certificate upon successful submission of a short data analysis report (up to 2,000 words) and Python code scripts showing all your workings,
You will be encouraged to network with other learners enrolled on the course – you will automatically become a member of the course forum/message board where you can ask general course questions and you will maintain your learner’s profile in which you can share your bio and social media links.
This instructor-led course duration is planned over 6 teaching weeks (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).
In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.
Start date: Thursday, 5th of November 2020 @14:30 London (UK) time
Schedule of sessions: Every Thursday at 14:30 London (UK) time for 6 weeks
Deadline for registrations: Monday, 2nd of November 2020 @ 17:00 London (UK) time
Week 1: Introduction to Machine Learning with Python
- Concepts, terminology and context: unsupervised vs. supervised vs. semi-supervised approaches,
- Overview of methods and applications,
- Preparing data for Machine Learning tasks: revision of probability distributions, data normalisation and standardisation techniques, feature engineering, dealing with missing values,
- Dimensionality reduction with Singular Value Decomposition, Principal Component Analysis and Factor Analysis.
Week 2: Unsupervised learning with clustering approaches
- K-means and k-medians clustering,
- Hierarchical clustering,
- Evaluating clustering solutions, describing clusters and estimating cluster profiles,
- Overview of other important clustering methods: mean-shift, DBSCAN, Gaussian mixtures.
Week 3: Predicting continuous data with linear and non-linear models
- Multiple linear regression and selecting suitable predictors with stepwise regression,
- Ridge and lasso regularisation,
- Regression metrics for model evaluation, comparing models,
- Polynomial regression, splines and generalised additive models (GAMs).
Week 4: Binary and multinomial classification – part 1: methods, evaluation metrics, model selection
- Introduction to classification with logistic regression – understanding probabilities and log-odds,
- Model selection and classification metrics: sensitivity, specificity, F score, Kappa, log-loss, R-squared etc.,
- Linear and quadratic discriminant analysis,
- Cross-validation and bootstrapping.
Week 5: Binary and multinomial classification – part 2: overview of other important approaches
- Stochastic Gradient Descent classifier,
- Distance-based classification: k-Nearest Neighbours algorithm,
- Probabilistic Naive Bayes classifier and kernel-based Support Vector Machines,
- Semi-automated and automated tuning of classification models.
Week 6: From decision trees to ensembles
- Classification and Regression Trees (CART),
- Estimating variable importance, bagging and boosting,
- Tree-based Random Forests ensemble,
- Extra Gradient Boosting (XGBoost) algorithm.
Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with Python data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.
Course pre-requisites and further instructions
We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.7 installed on their laptops (any operating system). As Anaconda’s Python is a free and fully-supported distribution you can download it directly from https://www.anaconda.com/distribution/. Please contact us should you have any questions or issues with the installation process.
We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. We suggest that the course is preceded with our “Python for Data Analysis” open-to-public tutor-led online training course.
Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) during the tutor-led video sessions.
You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions.
The primary spoken and written language of the course is English.
You are encouraged to complete the online Learner’s Skills Inventory to allow Mind Project and our course tutors to customise the course teaching style depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Learner’s Skills Inventory will be held fully-confidential and will only be used to provide a quality statistical computing and data science training.
Discounts and multiple bookings
Early Bird Offer allows you to save up to 15% off the total course enrolment price (on top of other discounts). This offer is usually valid up until 3-4 weeks before the start of the course.
We offer 2 types of enrolment options:
- Regular Fee – full-priced enrolment for learners representing commercial organisations or self-funded individuals who do not meet our eligibility criteria for discounted rates (please see below),
- Discounted Fee – applicable to undergraduate and postgraduate students as well as representatives of registered charitable organisations and non-governmental organisations (NGOs) – this category also includes employees of the National Health Service (NHS).
Students and individuals eligible for the Discounted Fee should submit a copy of their student or organisation ID card (with their name and card expiry date visible) when making the purchase of their place on the course for the discount eligibility verification purposes. Alternatively, the discount eligibility can be verified by submitting either i.) a copy of a letter from the university registrar or student’s department confirming your status, or ii.) a copy of a letter from your employer (on a company letter-headed paper with a charity/NGO registration number) which confirms your current position within the organisation.
Apart from the Early Bird Offer and discounted fees for students or employees of charitable organisations and NGOs, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.
- If you book 3 or 4 tickets on any of our tutor-led open-to-public online training courses, you will receive 5% discount on the total price of your booking.
- If you book 5 or more tickets on any of our tutor-led open-to-public online training courses, you will receive 10% discount on the total price of your booking.
All discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for your delegates at a discounted rate.
Arrange this course at your organisation
This open-to-public online course is a more generalised version of our fully-customisable in-house / online training course “Machine Learning with Python”. If your delegates cannot attend this public course, or you are interested in arranging this training course explicitly for your delegates (or at your premises) or simply you need a bespoke, made-to-measure training solution, please request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.
You may email us directly at info(at)mindproject.io and include the following information in your enquiry:
contact details to a person who should receive the quote,
number of delegates you would like to train,
approximate number of online sessions (or half-days / full days for on-site in-house course) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue if not online,
any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,
any other questions you may have.
If you don’t know the answers to questions above or you are at early stages of course planning, we would be happy to arrange an informal chat and help you choose the most suitable and budget-efficient option.