Friday, 17. May 2019, Venue is being confirmed. Stay tuned!, [3 Day Training Course] Machine Learning: Houston

Why this training?
This 3-day course will give you a comprehensive overview of various tools, frameworks, and concepts behind machine learning.
In just three days, you will get a clear understanding of the core machine learning techniques, mathematical concepts, and engineering solutions for daily usage. You will go through the complete process of building machine learning systems, from data understanding to modelling.
During hands-on labs, accompanying each theoretical unit, you will see the inner workings of a machine learning model and will reproduce the stages of its development life cycle.
At the end of the course, the participants will be able to design working scripts that can be used as a basis for creating algorithms to address business-specific challenges.


Who should attend?
This training is a good fit for anyone who has a basic understanding of Python and wants to acquire new skills in just 3 days

Course objectives
During the course the students will:


Gain a basic understanding of machine learning concepts


Learn how to use main troubleshooting techniques of machine learning


See the inner workings of a machine learning model and reproduce the stages of its development life cycle



Program
Day 1. Core Concepts and Techniques
Theory


An introduction to machine learning tasks and definitions


Core principles of building machine learning algorithms


A diversity of machine learning algorithms: from linear regression to random forest


Core Python packages for machine learning


Practice


Linear and logistic regressions


k-nearest neighbors and k-means


Decision trees and random forest


Handling classification, regression, and clustering tasks


*Packages of choice are Pandas/NumPy/scikit-learn
Day 2. Advanced Algorithms
Theory
Day 2 will cover the use of advanced theoretical concepts underlying such complex models as:


LASSO/Ridge (regularization)


PCA/SVD (dimensionality reduction)


Advanced clustering algorithms, such as DBSCAN, expectation-maximization (different similarity approaches to data)


Naive Bayes (The Bayes theorem)


Complex ensembling schemes, gradient boosting, stacking (iterative refinement)


Algorithmic hyperparameter tuning


Practice


LASSO


PCA


DBSCAN, expectation-maximization, agglomerative clustering, mean shift


Naive Bayes


Gradient boosting machine, stacking


Tree-structured Parzen estimator


*Packages of choice are Pandas/NumPy/scikit-learn/HyperOpt/XGBoost
Day 3. Feature Engineering and Development Methodology
Theory
A wide range of topics related to building ML models will be covered:


Feature engineering


Dealing with missing data and outliers


Dealing with imbalanced classification


Advanced validation schemes


Handling of model versioning


CRISP-DM as a major machine learning development methodology


Practice
Feature engineering:


Polynomial and logarithmic features, combinations of features


Periodic feature encoding


Target encodings


Imbalanced classification:


Advanced metrics for classification


Threshold tuning


Over- and undersampling (SMOTE)


Missing data handling:


Imputation of missing values using k-nearest neighbors or decision trees


Advanced validation:


Cross-validation for time series


*Packages of choice are Pandas/NumPy/scikit-learn
After participating in the course you will get a certificate of completion!









Prerequisites
Altoros recommends that all students have:
- Basic Python programming skills, a capability to work effectively with data structures
- Experience with the Jupyter Notebook applications
- Basic experience with Git
- A basic understanding of matrix vector operations and notation
- Basic knowledge of statistics
- Basic knowledge of command line operations
All code will be written in Python with the use of the following libraries:
- Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one.
- scikit-learn
- Matplotlib
All these libraries will be installed using Anaconda.
Requirements for the workstation:
- A web browser (Chrome/Firefox)
- Internet connection
- A firewall allowing outgoing connections on TCP ports 80 and 443
The following developer utilities should be installed:
- Anaconda
- Jupyter Notebook (will be installed using Anaconda)
If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

Payment info:
If you would like to get an invoice for your company to pay for this training, please email to training@altoros.com and provide us with the following info:


Name of your Company/Division which you would like to be invoiced;


Name of the person the invoice should be addressed to;


Mailing address;


Purchase order # to put on the invoice (if required by your company).


The tickets are limited, so hurry up to reserve your spot NOW!
! Please note our classes are contingent upon having 7 attendees. If we don't have enough tickets sold, we will cancel the training and refund your money one week prior to the for the understanding.



[3 Day Training Course] Machine Learning: Houston

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