Data science in practice: data analysis and machine learning with Python
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Machine Learning Engineer
1. introduction to Python for Data Science
- Why Python?
- Python basics taught in compact form
- Data types and data structures
- Use external packages
- Eigenvalue calculation with NumPy and Matplotlib
- Use of Jupyter notebooks
2. descriptive statistics with pandas
- Introduction to pandas
- Mean value
- Standard deviation
- Median
- Quantiles
- Analyze DataFrames with df.describe()
3. introduction to machine learning
- Methods, data types and data
- Model training and model evaluation
- Bias and variance
- Training and inference with scikit-learn
4. linear regression: models, training, extensions
- Linear regression
- Training and evaluation of regression models
- Linear regression with multiple features
- Linear regression with scikit-learn
- Categorical features
- Regression with basis functions
5. model evaluation and optimization
- Overfitting: cross-validation and regularization
- Regularization: Lasso and ridge regression
- Bias-Variance Tradeoff: Balancing errors
6. working with relational data
- Grouping
- Aggregation
- Transformation
- Filter
- Joins
- Cross product
7 Important classification methods
- K-Nearest Neighbors (k-NN)
- Decision Trees
- Ensemble methods (bagging, boosting, random forest)
- Support Vector Machines
8. optimization and evaluation of models
- Grid Search
- Cross-validation
- Evaluation of binary classifiers
9. methods in unsupervised learning
- k-Means
- Dimension reduction (PCA)
10. overview of neural networks
- How neural networks work
- Perceptrons
- Activation functions
- Gradient method
Practical exercises for co-programming
Throughout the training , you will solve practical tasks with Python that will help you to immediately apply and consolidate what you have learned. These exercises are designed to simulate challenges that data scientists face in the real world. The exercises are provided in Jupyter Notebooks - so you don't need complex programming environments.
Basic programming knowledge is an advantage
This training uses the Python programming language. Even though the training begins with a short crash course on important Python instructions, it is an advantage if you already have basic programming knowledge, for example of variables, lists, arrays and for loops, or if you have acquired some initial knowledge of these before the training . This will enable you to grasp the concepts taught more quickly and implement the practical exercises more effectively. You can also successfully complete the training without any prior knowledge. Please be prepared for a somewhat steeper learning curve. With or without prior knowledge, the trainers will provide you with the best possible support and guide you through the content.
You will get to know all stages of the data mining and machine learning process in detail - from data preparation to the training and application of data models.
This course will open up a new, sought-after field of expertise and qualify you for tasks in the areas of data analysis, machine learning and artificial intelligence.
After completing the seminar, you will be equipped with solid knowledge and practical skills to plan, design and implement data science projects yourself.
You will develop a clear idea of how you can use machine learning effectively in your day-to-day work to support specific use cases and create added value for your company.
This training is carried out in a group of a maximum of 12 participants. Individual support from the trainers is guaranteed.
The practical exercises are provided in the form of Jupyter notebooks, which you can easily work with locally on your computer or on a cloud platform of your choice such as Google Colab or Amazon Sagemaker.
Once you have registered, you will find all the information, downloads and extra services for this training course in your online learning environment.
This training is aimed at anyone who wants to understand machine learning in detail and use it in their own projects.
Basic knowledge of programming - in Python or another language - as well as advanced technical, mathematical and statistical knowledge is an advantage, but not required to participate in the course.
This course is a valuable building block in the qualification as a data scientist, data analyst, machine learning engineer and data engineer.
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