Private Training: Machine Learning for Advanced Analytics

We help businesses achieve more using Machine Learning and Data Science. Leverage the practical expertise of expert instructors to accelerate your projects.

While traditional business intelligence tools examine historical data, advanced analytics frameworks focus on forecasting future events and behaviors to discover deeper insights, enabling businesses to predict the effects of potential changes in their strategies.
In our course, we cover fundamental advanced analytic techniques, such as data mining and machine learning, feature engineering and representation learning, pattern recognition and forecasting, as well as cluster analysis and multivariate statistics. All these techniques are widely employed in multiple industries, including marketing, healthcare, telecom, risk management, and economics.
Hands-on
Hands-on

Each theoretical block is followed by a relevant set of practical labs

Instructor-led
Instructor-led

You'll be guided by an experienced instructor during all training days

On-site
On-site

The training is delivered at your premises and on suitable dates

Hands-On Training Course: Machine Learning for Advanced Analytics

Adopt modern frameworks for predictive analytics
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
Advanced Topics
Theory
  • LASSO/Ridge (regularization)
  • PCA/SVD (dimensionality reduction)
  • Advanced clustering algorithms, such as DBSCAN and 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, and mean shift
  • Naive Bayes
  • Gradient boosting machine, stacking
  • Tree-structured Parzen estimator
Packages of choice are Pandas/NumPy/scikit-learn/HyperOpt/XGBoost
Feature Engineering and Development Methodology
Theory
  • Feature engineering
  • Dealing with missing data and outliers
  • Dealing with imbalanced classification
  • Advanced validation schemes
  • Handling model versioning
  • CRISP-DM as a major machine learning development methodology
Practice
  • Feature engineering: polynomial and logarithmic features, combinations of features, periodic feature encoding, and target encodings
  • Imbalanced сlassification: advanced metrics for classification, threshold tuning, as well as 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
Tabular Data
Transaction data is a largely prevalent type of data sets, especially in telecom/banking. The purpose of this module is to demonstrate an approach to this data that helps to retrieve useful insights.
  • Preparation of transactional data
  • A time-series family of algorithms
  • Statistical and neural network approaches
Introduction to Deep Learning
We'll look at surprisingly strong machine learning techniques that have recently gained popularity and cover the following topics:
  • Structure of neural and feedforward neural networks
  • A mechanism for learning neural networks
  • The means of managing the neural network learning process

This program doesn't meet your requirements? Combine your own training program!

Combine

What Attendees Say

Here's what attendees say about our events
Neural Networks and Deep Learning
ML-01
DL-01
DL-02
DL-03
DL-04
Combination of conceptual and practical contents. Also, shared personal experiences and views were particularly valuable.
Arpad Rozsas, Neural Networks and Deep Learning course in Madrid
Deep Learning for Advanced Analytics
ML-01
BD-01
BD-02
DL-01
It was very well designed and delivered in steps.
Rajender Aluguvelli, Machine Learning and Big Data Training in Irving
Modern Applications of Machine Learning
ML-01
DL-01
Workshop
I enjoyed the class and learned a lot even though there was so much content jammed into a very small time. The most enjoyable was deep neural nets and seeing some of the largest example. The most valuable thing professionally will probably be the classification clustering that I have learned K-NN probably
Mark, Machine Learning training in Washington DC

Contact us

Share your training requirements or ask questions and we will contact you shortly!

Alexandra Mironova

Alexandra Mironova

Training Coordinator

Headquarters

location icon830 Stewart Dr., Suite 119Sunnyvale, CA 94085
First Name*
Last Name*
Email*
Phone*
Your company name*
How can we help you?