Machine Learning Course

Three days of intensive theory and practice to level up your machine learning skills. Get a deep understanding of tools, frameworks, and concepts while creating algorithms that address your business-specific challenges right in the classroom.

Machine Learning Course


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Why enroll

  • Gain a basic understanding of machine learning concepts
  • Learn how to use main troubleshooting techniques of machine learning
  • Go through the complete process of building machine learning systems
  • Get real scripts which can be used as a basis for creating algorithms, which address business-specific challenges

Who should attend

  • Engineers who want to get familiar with basic principles of machine learning
  • Engineers who feel enthusiastic about diving into the world of various machine learning tools and frameworks
  • Everyone who wants to become a fully skilled ML engineer without wasting months on it

Training program

see details
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
Core Concepts and Techniques (Labs)
  • 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
see details
Advanced Algorithms (Theory)
  • 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
Advanced Algorithms (Labs)
  • 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
see details
Feature Engineering and Development Methodology (Theory)
  • 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
Feature Engineering and Development Methodology (Labs)
  • 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
See details

Altoros recommends that all students have:

  • Programming: 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 matrix vector operations and notation
  • A basic knowledge of statistics
  • Basic 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

A workstation with the following capabilities:

  • 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)

All these libraries 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.


  • $500 Early Bird

    Sales end 50 days before the course in your city starts.
  • $750 Standard

    Sales end 30 days before the course in your city starts.
  • $1,000 Last Minute

    Sales end 7 days before the course in your city starts.
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Our customers

Here is what our customers say about us
Biggest value of the course? Combination of conceptual and practical contents. Showing the state-of-the art achievements and hence developing a feeling what can be achieved with DNN
What did you like most at the training?
Both high level and the details of machine learning
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
Great experience! Very knowledgeable and friendly trainers. Biggest value of the course - practical examples/issues the trainers provided based on their experience
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Contact us

Alexandra Mironova

Alexandra Mironova

Training Coordinator


location icon830 Stewart Dr., Suite 119Sunnyvale, CA 94085
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