|Main advantage||An opportunity to quickly verify Machine Learning usefulness in your company. Learning skills to solve simple problems using Machine Learning.|
|Duration||a) half-days: 10 days x 3 hours 30 minutes (including breaks)|
b) full-days: 5 days x 7 hours (including breaks)
|Format||workshop (70% workshop / 30% lecture)|
|Venue||a) online or|
b) client’s office or other place chosen by the client, in Europe
|Enrollment||in-house course for a group of people within one company|
|Group size||up to 10 delegates|
|Course language||English, Polish or both during the same training|
- Python 3 vs Python 2
- Python 3.x Installation
- PyCharm – IDE
- Executing Python Scripts
- pip – Packet Manager
- IPython – Interactive Console
- Jupyter Notebook
- virtualenv – Isolated Python Installations
- Tooling Summary
- Tooling for Data Science
- Data Visualisation with matplotlib
- Basic Line Plots
- More Series Customization
- Log and Symlog Scale
- Multiple Plots
- Python Crash Course
- Data Types
- Introduction to Machine Learning
- What is Machine Learning?
- Basic Concepts
- Problem Types
- Basic Questions
- Common Workflow
- Algorithm Cheat-Sheet
- Supervised Learning
- Bias-Variance Trade Off
- Case Study: Iris Classification
- Business Applications
- Regression Linear Models
- Simple Linear Regression
- Multiple Linear Regression
- Ridge Regularisation
- Lasso Regularisation
- ElasticNet Regularisation
- Feature Engineering & Selection
- One Hot Encoding
- Polynominal & Interaction Terms
- ln(x+1) Transformation
- Feature Selection
- Cross Validation and Grid Search
- Cross Validation
- Cross Validation Strategies
- Grid Search
- Logistic Regression
- Binary Classification
- Multiclass Classification
- Evaluation for Model Selection
- k-Nearest Neighbors
- Linear & Logistic Regression
- Lasso, Ridge and ElasticNet Regularization Recap
- Neural Networks
- Support Vector Machines
- Kernelized Support Vector Machines
- Decision Trees
- Random Forests and Boosting
- Classificators Comparison
- k-Means Clustering
- Agglomerative Clustering
- Hierarchical Clustering and Dendograms
- Evaluating Clustering with Ground Truth
- Comparing Clustering on Digits
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Non-negative matrix factorisation (NMA)
- Decomposing Signals with NMF
- Manifold Learning with t-SNE
Benefits for the Sponsor
As the course sponsor or HR you get:
- Analysis of the needs and my help to choose or design a great course during a phone call with the sponsor, HR, team leader or/and course delegates. On top of that, we ask delegates on the very first day what their needs are, to make even better usage of the course time.
- Course customisation to your needs.
- Guarantee that the course is conducted by an expert that worked for Google.
- Course evaluation as an electronic form at the end of the last course day. The evaluation results are sent to interested people (most of often they’re course sponsor and HR).
- Simple communication – you can contact the trainer directly by phone or email.
- Easy buying procedure – one call or email is enough to get offer and to book a date. I don’t do overbooking. The course is confirmed once you send the Purchase Order.
- Friendly business partner – as a rule, I treat all my clients like friends. I don’t build walls, I’m not pretending to be a huge training company and I write in first person.
Clients very often decide to order other training (including dedicated courses) after observing positive results of this course.
Benefits for Delegates
Delegates will benefit because of:
- Support after the course, via email and phone.
- Setup instruction before the course to save time at the beginning of the course. I’m happy to help you via email, phone or Skype, zoom.us etc. in case of any questions or issues.
- Course materials consisting of code snippets, comments, exercises and solutions. The entire courseware is a single web page which make it very easy to lookup something there. Courseware is available online during and after the training. Delegates can download it to use it offline. Courseware can be updated during the course in real time, so that we can include comments or entire new sections suggested by delegates.
- Environment ready to use after the course – we don’t use virtual machines. Instead, we install everything on delegates machines, so that they can reuse the same setup after the course.
- Recording of the training (in case of online training)
Below you can find some references.
Very inspiring training. I really appreciate the way Chris managed to walk us through the complex world of machine learning using Python. Good course materials updated real time. Highly recommend.
Finance Director at DNB Bank Polska S.A.
Well prepared training and reasonably passed knowledge, thanks to which we develop better services.
Infrastructure Team Manager at allegro.pl
Chris recently taught a four day class on Machine Learning with Python four our team. The class was very good with the right balance of theory and practice. I cannot think of a better way to give a four day class about such an extensive topic.
Head of Krakow Product Control Analytics at HSBC
You can read more references here.