Data Analysis and Machine Learning Training

Catalogue index: IFS/53

  • In stock
  • Delivery: free
  • Dedicated training:
  • Producer:
  • Training level:
    • Średniozaawansowany
  • Type of training:
    • stacjonarnie; online
PLN2,326.67 net / particip. PLN34,900.05  net for 1 workshop PLN2,861.80 gross incl. 23% VAT / particip.

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Features

Features
Additional materials:
  •  
Certification:
  • TAK
Dedicated training:
Number of training hours:
  • 40
Producer:
Training language:
  • polski
Training level:
  • Średniozaawansowany
Type of training:
  • stacjonarnie; online

Description

Company Description

InfoShare Academy is a leading IT academy offering comprehensive educational programs in new technologies for companies. Since 2015, we have supported organizations in developing technology teams through dedicated courses in Machine Learning, DevOps, Data Engineering, Python, UX/UI Design, AWS, and Kubernetes. Our training is based on practical skills and real business cases. We collaborate with over 300 industry practitioners, ensuring that our programs are tailored to current market needs. We specialize in reskilling and upskilling employees. With us, you will build effective teams implementing new technologies that will accelerate innovation and strengthen your company's competitiveness in the market. Check out our training offerings designed to develop your employees' IT competencies.

Training Description

Below we present a sample training program that can be modified according to the expectations and level of the training group. Before preparing the final training program, we conduct a technical conversation involving the trainer and a technical person or the entire team of developers representing the client to establish the details of the training.

Who is the training for
  • For individuals developing towards working with machine learning and artificial intelligence.
  • For data analysts needing tools for implementing and automating their analyses and algorithms.
  • For Python programmers looking to expand their competencies in data analysis and machine learning.
Goals
  • Acquire skills in data analysis and machine learning using Python libraries such as Pandas, NumPy, SciPy, matplotlib, and seaborn for effective data processing, analysis, and visualization.
  • Gain knowledge about working with neural networks in TensorFlow and Keras, including creating, training processes, fine-tuning, transfer learning, and types of networks used in image and language processing.
  • Learn the process of creating models in the Scikit Learn library, including the learning process and hyperparameter tuning.
Benefits
  • You will learn to retrieve data, conduct analysis, perform various operations on data, including handling missing data and data cleaning procedures.
  • You will master data visualization techniques, including ways to present data and export and save visualizations.
  • You will learn about deploying models, including theoretical issues related to monitoring and daily work with machine learning.
  • You will understand practical approaches to classification, regression, and clustering problems.
Training Program

  1. Computational and algorithmic tools (Pandas, NumPy, and SciPy libraries)

    • Data retrieval

    • Analysis and methods of operation and function execution on data

    • Data operations – working with missing values

    • Data cleaning procedures

  2. Visualization (Matplotlib, Seaborn libraries)

    • Data visualization, presentation methods (Matplotlib, Seaborn)

    • Exporting results, saving visualizations

  3. Working with API resources and databases (as technical capabilities allow). Machine Learning and Deep Learning in Python

    • Model creation process in the Scikit Learn library (learning process, model hyperparameters, working with classification problems)

    • Model creation process in the Scikit Learn library (learning process, model hyperparameters, working with classification and regression problems)

  4. Working with API resources and databases (as technical capabilities allow). Machine Learning and Deep Learning in Python

    • Model creation process in the Scikit Learn library (learning process, model hyperparameters, working with regression and clustering problems, model comparison)

    • Working with neural networks in TensorFlow and Keras (creation, training process, fine-tuning, and transfer learning, types of networks in image and language processing)

    • Working with neural networks in TensorFlow and Keras (creation, training process, fine-tuning, and transfer learning, types of networks in image and language processing)

    • Model deployment – theoretical issues related to monitoring and daily work with machine learning

Duration

40 h/ 5 days

Price includes
  • Certificate of completion
  • Monthly access to training recordings (in case of online format)
  • Customization of the training program to client needs

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