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Course Outline
- Machine Learning introduction
- Types of Machine learning – supervised vs unsupervised learning
- From Statistical learning to Machine learning
- The Data Mining workflow:
- Business understanding
- Data Understanding
- Data preparation
- Modelling
- Evaluation
- Deployment
- Machine learning algorithms
- Choosing appropriate algorithm to the problem
- Overfitting and bias-variance tradeoff in ML
- ML libraries and programming languages
- Why use a programming language
- Choosing between R and Python
- Python crash course
- Python resources
- Python Libraries for Machine learning
- Jupyter notebooks and interactive coding
- Testing ML algorithms
- Generalization and overfitting
- Avoiding overfitting
- Holdout method
- Cross-Validation
- Bootstrapping
- Evaluating numerical predictions
- Measures of accuracy: ME, MSE, RMSE, MAPE
- Parameter and prediction stability
- Evaluating classification algorithms
- Accuracy and its problems
- The confusion matrix
- Unbalanced classes problem
- Visualizing model performance
- Profit curve
- ROC curve
- Lift curve
- Model selection
- Model tuning – grid search strategies
- Examples in Python
- Data preparation
- Data import and storage
- Understand the data – basic explorations
- Data manipulations with pandas library
- Data transformations – Data wrangling
- Exploratory analysis
- Missing observations – detection and solutions
- Outliers – detection and strategies
- Standarization, normalization, binarization
- Qualitative data recoding
- Examples in Python
- Classification
- Binary vs multiclass classification
- Classification via mathematical functions
- Linear discriminant functions
- Quadratic discriminant functions
- Logistic regression and probability approach
- k-nearest neighbors
- Naïve Bayes
- Decision trees
- CART
- Bagging
- Random Forests
- Boosting
- Xgboost
- Support Vector Machines and kernels
- Maximal Margin Classifier
- Support Vector Machine
- Ensemble learning
- Examples in Python
- Regression and numerical prediction
- Least squares estimation
- Variables selection techniques
- Regularization and stability- L1, L2
- Nonlinearities and generalized least squares
- Polynomial regression
- Regression splines
- Regression trees
- Examples in Python
- Unsupervised learning
- Clustering
- Centroid-based clustering – k-means, k-medoids, PAM, CLARA
- Hierarchical clustering – Diana, Agnes
- Model-based clustering - EM
- Self organising maps
- Clusters evaluation and assessment
- Dimensionality reduction
- Principal component analysis and factor analysis
- Singular value decomposition
- Multidimensional Scaling
- Examples in Python
- Clustering
- Text mining
- Preprocessing data
- The bag-of-words model
- Stemming and lemmization
- Analyzing word frequencies
- Sentiment analysis
- Creating word clouds
- Examples in Python
- Recommendations engines and collaborative filtering
- Recommendation data
- User-based collaborative filtering
- Item-based collaborative filtering
- Examples in Python
- Association pattern mining
- Frequent itemsets algorithm
- Market basket analysis
- Examples in Python
- Outlier Analysis
- Extreme value analysis
- Distance-based outlier detection
- Density-based methods
- High-dimensional outlier detection
- Examples in Python
- Machine Learning case study
- Business problem understanding
- Data preprocessing
- Algorithm selection and tuning
- Evaluation of findings
- Deployment
Requirements
Knowledge and awareness of Machine Learning fundamentals
Testimonials
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
Bardzo merytoryczne szkolenie, bardzo duża wiedza prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Humor prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Wiedza i umiejetnosc jej przekazania
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
The details and the presentation style.
Cristian Mititean - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Interactive, a lot of exercises
Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
The Exercises
Khaled Altawallbeh - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Going through the notebooks, becoming more familiar with Qiskit and the various ways to do things.
Bank of Canada
Practical Quantum Computing Course
Very very competent trainer who know how to adapt to his audience, and to solve problems Interactive presentation
OLEA MEDICAL
MLflow Course
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
MLflow Course
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Sebastian Pawłowski, Bitcomp Sp. z o.o.
Machine Learning on iOS Course
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Kubeflow Course
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Machine Learning Concepts for Entrepreneurs and Managers Course
Convolution filter
Francesco Ferrara - Inpeco SpA
Introduction to Machine Learning Course
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kubeflow Course
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Applied AI from Scratch in Python Course
I like that it focuses more on the how-to of the different text summarization methods
Text Summarization with Python Course
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Machine Learning Course
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
Machine Learning Course
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Machine Learning Course
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Data Mining & Machine Learning with R Course
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Artificial Neural Networks, Machine Learning, Deep Thinking Course
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