Machine Learning

Course Info

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-day sessions: 10 days x 3 hours 30 minutes (including breaks) b) full-day sessions: 5 days x 7 hours (including breaks)

1. Tooling
   1. Python 3 vs Python 2
   2. Python 3.x Installation
   3. PyCharm – IDE
   4. Executing Python Scripts
   5. pip – Packet Manager
   6. IPython – Interactive Console
   7. Jupyter Notebook
   8. virtualenv – Isolated Python Installations
   9. Tooling Summary
   10. Tooling for Data Science
2. Data Visualisation with matplotlib
   1. Basic Line Plots
   2. More Series Customization
   3. Log and Symlog Scale
   4. Multiple Plots
3. Python Crash Course
   1. Lists
   2. Dictionaries
   3. Data Types
4. Introduction to Machine Learning
   1. What is Machine Learning?
   2. Basic Concepts
   3. Problem Types
   4. Basic Questions
   5. Common Workflow
   6. Algorithm Cheat Sheet
   7. Supervised Learning
   8. Bias-Variance Trade Off
   9. Case Study: Iris Classification
   10. Business Applications
5. Linear Regression Models
   1. Simple Linear Regression
   2. Multiple Linear Regression
   3. Ridge Regularisation
   4. Lasso Regularisation
   5. ElasticNet Regularisation
6. Feature Engineering & Selection
   1. Pipelines
   2. One Hot Encoding
   3. Polynominal & Interaction Terms
   4. ln(x+1) Transformation
   5. Feature Selection
7. Cross Validation and Grid Search
   1. Cross Validation
   2. Cross Validation Strategies
   3. Grid Search
8. Classification
   1. Logistic Regression
   2. Binary Classification
   3. Multiclass Classification
   4. Evaluation for Model Selection
9. Models
   1. k-Nearest Neighbors
   2. Linear & Logistic Regression
   3. Lasso, Ridge and ElasticNet Regularization Recap
   4. Neural Networks
   5. Support Vector Machines
   6. Kernelized Support Vector Machines
   7. Decision Trees
   8. Random Forests and Boosting
   9. Classificators Comparison
10. Clustering
    1. k-Means Clustering
    2. Agglomerative Clustering
    3. Hierarchical Clustering and Dendograms
    4. DBSCAN
    5. Evaluating Clustering with Ground Truth
    6. Comparing Clustering on Digits
11. Dimensionality Reduction
    1. Principal Component Analysis (PCA)
    2. Non-negative matrix factorisation (NMA)
    3. Decomposing Signals with NMF
    4. Manifold Learning with t-SNE

Collaboration Process

1. Form. You fill out the form at the bottom of the page.
2. Free Consultation. We arrange a free, non-binding online meeting that should last no longer than 50 minutes. From the first contact, full confidentiality is observed, and if necessary, we can start by signing an NDA. The purpose of this conversation is to thoroughly discuss your situation and needs, so we can prepare a solution for you in the next step.
3. Solution Proposal + Follow-up Consultation. After the consultation, we analyze your situation and prepare a tailored solution proposal (training and/or consulting). We discuss this proposal during another free consultation.
4. Formalities. Signing the contract and making the payment (prepayment). The training date is reserved upon payment.
5. Training Sample. If needed, we can precede the main training with a 60-minute training sample to ensure that it is worth your while to cooperate with us.
6. Training and/or Consulting Activities.
7. Post-training Support – tailored individually to your needs.

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.

Krzysztof Gębal
Finance Director at DNB Bank Polska S.A.

Well prepared training and reasonably passed knowledge, thanks to which we develop better services.

Arkadiusz Baraniecki
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.

Nicolas Leveroni
Head of Krakow Product Control Analytics at HSBC

See more references ->

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