Comprehensive Machine Learning Training
Level
BasicDuration
45h / 6 daysDate
Individually arrangedPrice
Individually arrangedComprehensive Machine Learning Training
The ever-increasing amount of data generated by people has led to a situation where traditional tools are no longer sufficient. Moreover, there is no indication that this number will decrease, which confirms the thesis that the demand for machine learning specialists will continue to grow. The popularity of the term “Machine Learning” in Google Trends shows that interest in the subject is rising at an enormous pace.
Who is this training for?
For developers, data analysts, business analysts, marketers, designers, and anyone for whom machine learning can significantly facilitate their work-
For people who already know a bit about data processing and analysis – this will make it easier to understand the material presented
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For those with basic experience in programming in Python
What will you learn during this training?
- You will use Python in machine learning projects – leveraging libraries such as Pandas, NumPy, scikit-learn, and Matplotlib
- After getting familiar with the essential tools and libraries, you will move on to further learning, including working with files, data cleaning, and machine learning models
- You will learn practices that allow better management of code and project structure when building web applications
- While writing code, you will pay particular attention to the possibility of integrating it with code written by other people
- You will learn the language of developers, concepts, principles, and best practices of working with data, as well as how to communicate effectively in a programming team
Training Program
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Module 1: Introduction to Machine Learning
- Introduction to machine learning
- Key concepts
- The importance of splitting data into training, validation, and test sets
- Different types of machine learning
- Data as features
- Qualitative vs. quantitative data
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Module 2: Data Processing and Analysis
- Introduction to libraries: Pandas, NumPy, Matplotlib, scikit-learn
- Working with files
- Data cleaning
- Data wrangling
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Module 3: Jupyter Notebook
- Interactive environment
- Python virtual environments
- Cells, code, markdown
- Widgets
- IPython
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Module 4: Machine Learning Models
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- XGBoost
- Naive Bayes
- KNN classification
- SVM
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Module 5: Model Comparison and Consolidation
- Methods for selecting models for specific use cases
- Comparing different models
- Practical exercises
Contact us
we will organize training for you tailored to your needs
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