Machine Learning for analysts

Course ID : AI-004

Duration In-class (в days) : 40 academic hours

Duration Online : 40 academic hours

Сurriculum : in-class, Virtual Instructor-Led Training - ONLINE

Overview

The Machine Learning for analysts course is an intensive, hands-on program designed to master data analysis and machine learning techniques. Students will learn to apply ML to real-world business problems, including forecasting, classification, clustering, and time series analysis.

The Machine Learning and Data Analysis course is based on examples from various economic sectors, including retail, fintech, and marketing. You will master the full data science cycle: from data collection and cleaning to model building and implementation.

Students will learn to apply ML to data analysis. During the course, students will gain skills in optimizing and improving the quality of reporting and analytical materials, as well as identifying patterns and regulatory factors.

Outcomes

Students will master machine learning methods to solve real-world problems. The practical skills you will gain include:

  • Building and validating machine learning models on real-world data
  • Working with Python libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras
  • Analyzing data and identifying hidden patterns in business metrics
  • Creating ML models to predict sales, churn, and LTV
  • Visualizing analysis results using Matplotlib and Seaborn
  • Preparing and cleaning data for machine learning
  • Evaluating the business performance of ML models

Outline

1. Machine Learning Tasks. Python Libraries for Machine Learning: Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, NLTK, Natasha, Pandas
2. Features and Working with Them
3. Preprocessing and Feature Extraction Methods in Text Analysis
4. Embeddings in Text Analysis and Named Entity Extraction
5. Validation and Model Quality Assessment
6. Regression Methods
7. Classification Methods
9. Clustering Methods
10. Dimensionality Reduction Methods
11. Time Series Analysis Methods
12. Ensembles
13. Overview of Modern Pretrained Language Models

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