Applied Machine Learning

Get an in-depth, hands-on experience in solving Machine Learning use-cases from a beginner to advanced level. Learn to define projects and their requirements with a particular focus on quality testing of such algorithms.

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Course Report

4.97 / 5

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4.97 / 5

Part-Time

weeks

Remote

English

Program overview

Machine Learning is one of the fundamental blocks of Data Science. Its methods are being actively applied across various industries. The goal of this course is to teach you how to successfully apply Machine Learning to real-world business problems while avoiding common pitfalls.

By the end of the course you will be able to:

Convert a business problem into a Machine Learning problem.
Define requirements for a Machine Learning project (including key performance indicators) using an ML canvas.
Create different types of Machine Learning pipelines (supervised and unsupervised), including data transformation, feature engineering, building a data pipeline, hyper-parameter tuning, loss functions, and cross-validations on several regression and classification tasks.
Identify bias and fairness of Machine Learning problems and Machine Learning model explainability and interpretability.
Design and solve several real-world Machine Learning use-cases, e.g., predictive maintenance, churn prediction, customer segmentation.

Upcoming dates

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Course dates

Tuition

The next date is not yet known

Schedule: Mon & Wed, 18:00 - 21:00 (CET), remote

Looking for financing? Check out our financing options.

Where our students work

Get your dream job - we'll support you along the way!

What you will learn

End-to-End Machine Learning Pipelines

Day 1: Introduction - Start with how to define a Machine Learning use-case using an ML project canvas and learn about the industry standard lifecycle for Data Science and Machine Learning problems, CRISP-DM. We will then l provide an overview of the major steps of an end-to-end ML pipeline, including defining a base model, defining KPIs of success, different error metrics, model assessment/selection, train-test split, data cleaning, and transformation pipelines, under vs. over-fitting, performance metrics for regression tasks.

Day 2: End-to-End ML pipeline - Building upon concepts from day 1, you will apply feature normalization, missing value imputation, scaling, and selection pipelines. You will work with linear and polynomial regression models and regularization techniques (ridge, lasso, and elastic net).

Classification Models

Day 1: Classification Models - Practice data labeling techniques and applications of Machine Learning algorithms for classification tasks using logistic regression. You will also be working with concepts such as class-imbalance, precision-recall analysis (false positive vs. false negative rates), ROC curves, and the use of the confusion matrix.

Day 2: Advanced Classification Models - Go deeper into Supervised Machine Learning problems using commonly used classification algorithms like K-Neighbors, Naive Bayes, Support Vector, Decision Trees and state-of-the-art Ensemble Learning techniques such as Random Forest, XGBoost and CatBoost.

Unsupervised Learning and Ensemble Models

Day 1: Unsupervised Learning - Along with understanding K-means, hierarchical clustering, and DBSCAN, learn how to apply these models to identify an optimal number of clusters, generate and interpret cluster labels using visualizations and statistics. Another area of focus will be applying dimensionality reduction techniques like Principal Component Analysis and t-SNE.

Day 2: Machine Learning Use Case - Moving back to classification models (now with the power of unsupervised learning), you will apply your skills in a real world scenario. Another focus of this day is to see how unsupervised learning, combined with supervised learning, increases the power of ML systems.

Model Explainability and Selection

Day 1: Model Bias and Explainability Analysis - Most Machine Learning models are biased. We will explore the steps you need to take towards making the model accountable to its results. You will apply visualization techniques to understand model bias and perform model interpretability and explainability analysis using state-of-the-art frameworks like LIME and SHAP. You will also learn about the limitations of these methods and how to identify the effect of confounding factors in such interpretations.

Day 2: Hyperparameter Tuning and AUTO-ML - Though you have been developing and using your own Machine Learning pipelines, on this day, you will learn and apply how to automate most components of the Machine Learning model development and evaluation lifecycle using state-of-the-art AUTO-ML frameworks like PyCaret, TPOT and MLJAR.

Model Deployment, Experiment Tracking and Monitoring

Day 1: ML Model Deployment - Now that we have learned all about model development, it’s time to move on to the next step in the ML process cycle - deployment. You will get familiar with git and version control systems, learn about model deployment strategies, key steps to building and deploying a Machine Learning model on the cloud using AWS as well as locally using a virtual environment.

Day 2: Experiment Tracking and Model Monitoring - Any Machine Learning model needs to be monitored after being deployed in production (even rarely used models need to be quality-checked). During the last lecture day of this course, you will apply statistical techniques to monitor model performance and quantify data drift.

Mini Project

Day 1: Bring your personal project and implement your complete Machine Learning pipeline. We will provide you with suggestions.

Day 2: Continuation and short presentations of final projects.

Weekly schedule

(CET)

Tue

Wed

Thu

Sat

09H00

12H00

13H00

17H40

18H00

19H00

21H00

Q&A Session

During these sessions, you are totally free to connect and ask any questions about the covered topics.

Lecture

Learn from our instructors who are experts in their respective fields and get introduced to new topics during live lectures.

Practice

Work on a set of interesting and challenging exercises related to the topics covered in the previous lesson.

Tools we teach

Python
Jupyter notebooks
Pandas
Matplotlib
Seaborn
Scikit-Learn
Auto-ML (TPOT, PyCaret, MLJAR)
Evidently
Flask
AWS

Application process and prerequisites

This course is suitable for beginners and intermediate Python programmers.

Simply apply to the program here.

FAQs

How is this course different from other self-paced online courses?

Most self-paced courses are a good place to start. However, they do not go into the depths of problems, people working with Machine Learning face. This course covers that and goes beyond any available Machine Learning training by focusing on both the conceptual and hands-on applied aspects of machine learning. Besides this, the instructors will also share tips, tricks and pitfalls from their industry expertise on machine learning.

Is there an interview selection for this course?

No, the goal is to introduce you to the field of applied Machine Learning. Anyone with a background in Python (programming) and basic high school math should be able to follow this course.

How does SIT Learning’s curriculum differ from other schools?

SIT Learning has trained more than 500 Full-Stack Engineers and Data Scientists. This has provided the SIT Learning team an in-depth understanding of what skills are most in-demand in the market and which parts of technology are of highest importance when working with data.

Who should attend this course?

Anyone who wants to gain experience in applying Machine Learning to business problems and learn about the challenges, pitfalls, and best practices of this field. People who want to work on concrete ideas to solve problems or to be able to talk with Data Scientists.

What are the prerequisites for this course?

Programming: The candidate should have beginner to intermediate Python Programming experience. Previous exposure to Machine Learning is not needed. Mathematics: This course deals with applying Machine Learning methods to concrete problems. Basic knowledge of linear algebra and calculus will help you understand the details of Machine Learning algorithms; however, it is not essential to follow this course.

How is the day by day schedule?

Every session, you will learn about a new topic during a 60 to 90 minutes-long lecture coupled with hands-on tutorials. For the rest of the 3 hours, you will train on guided exercises with our teachers and teaching assistants’ help. During Q&A sessions (on Thursdays and 20 minutes before each course), you are totally free to connect and ask any questions on the course and exercises, or for your personal projects.

Earn a Certificate of Accomplishment

Share your Certificate on social networks, printed resumes, CVs or other documents. Boost your career with the new skills that you gained.

Financing options

At Constructor Learning, we believe that finances should never be a barrier to accessing the education and training that can help individuals achieve their goals. That's why we offer a variety of financing options to make our courses more accessible to a diverse range of students. We also work with external organizations that provide financial assistance to those in need.

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Students say

Vaios Vlachos

Machine Learning

❝Right after the course I was able to start working on Machine Learning projects in my company.❞

Job:Data Scientist at Nispera

Akos Redey

Machine Learning

❝It was the best decision I have made by selecting this course over an MOOC at a known global provider.❞

Job:Senior Business Intelligence Analyst at Wüest Partner

Upcoming events

Attend one of our events. Discover our upcoming workshops, info sessions, final presentations and webinars on trending topics.

Final presentations of our Full-stack and Data Science students
28. Jul 23, 06:00 PM - 08:00 PM GMT+2
Flössergasse 2, 81369 Munich
Get ready to witness the ultimate showdown of brainpower and creativity as our bootcamp graduates present their final projects to a jam-packed audience of students, alumni, family members, friends, and companies! These incredible projects were developed in just three weeks, as the culmination of a three-month training period. But wait, there's more! Constructor Learning cordially invites you to join us for this epic event, where you'll have the chance to marvel at these exciting projects and soak up some serious inspiration. Don't miss out on this incredible opportunity to witness the future of tech firsthand. Register now!
Details
Final presentations of our Full-stack and Data Science students
28. Sep 23, 06:00 PM - 08:00 PM GMT+2
Flössergasse 2, 81369 Munich
Get ready to witness the ultimate showdown of brainpower and creativity as our bootcamp graduates present their final projects to a jam-packed audience of students, alumni, family members, friends, and companies! These incredible projects were developed in just three weeks, as the culmination of a three-month training period. But wait, there's more! Constructor Learning cordially invites you to join us for this epic event, where you'll have the chance to marvel at these exciting projects and soak up some serious inspiration. Don't miss out on this incredible opportunity to witness the future of tech firsthand. Register now!
Details

Your instructors

Dipanjan Sarkar

Lead Data Scientist & Instructor

Bio

Dipanjan (DJ) is a Lead Data Science Consultant & Instructor, leading advanced analytics efforts around Computer Vision, Natural Language Processing and Deep Learning. He is also a Google Developer Expert in Machine Learning. Dipanjan has advised and worked with several startups as well as Fortune 500 companies and is also a published author, having authored several books on R, Python, Machine Learning, Natural Language Processing, and Deep Learning. He loves sharing his knowledge with the community to help them grow in their own journey in Data Science.

Badru Stanicki

Instructor

Bio

With a Masters in Physics, Badru got into scientific programming and Data Science during his time at the German Aerospace Center in Spain. After working several years in research, he moved into Data Science, first as a student and then as a team member. His main interests are DataOps and Time Series Analysis.

Dr. Marie Bocher

Data Science Consultant

Bio

Marie has 7 years of experience in developing, deploying, and teaching machine learning and statistical models. At SIT, she consults companies and mentors individuals on various data science and software engineering topics. She is dedicated to sharing her expertise on these topics with a hands-on, interactive approach to teaching.