Data Analysis and Machine Learning Training
Level
IntermediateDuration
40h / 5 daysDate
Individually arrangedPrice
Individually arrangedData Analysis and Machine Learning Training
This training presents a sample program that can be tailored to the group’s expectations and skill level. Before finalizing the training agenda, we conduct a technical interview with the trainer and a technical representative or the client’s development team to adjust the content.
What You Will Learn
- Perform data analysis and machine learning with Python libraries: Pandas, NumPy, SciPy, matplotlib, seaborn
- Acquire data, perform analysis, handle missing data, and apply cleaning procedures
- Use visualization techniques (matplotlib, seaborn), export results, and save visualizations
- Build models in Scikit-learn: training, hyperparameter tuning, solving classification, regression, and clustering problems
- Work with neural networks in TensorFlow and Keras: building, training, fine-tuning, transfer learning, and applying models for image and language processing
- Learn about model productionization: theory of monitoring and daily operations with machine learning models
Who is this training for?
People developing toward machine learning and artificial intelligence-
Data analysts needing tools to implement and automate their own analyses and algorithms
-
Python programmers looking to expand their competencies in data analysis and machine learning
Training Program
-
Computational and Algorithmic Tools (Pandas, NumPy, SciPy)
- Data acquisition
- Data analysis and functions
- Data operations – handling missing data
- Data cleaning procedures
-
Visualization (matplotlib, seaborn)
- Data visualization and presentation methods
- Exporting and saving visualizations
-
Working with APIs and Databases (as technically feasible)
-
Machine Learning and Deep Learning in Python
- Model creation in Scikit-learn (training, hyperparameters, classification and regression problems)
- Model creation in Scikit-learn (regression, clustering, model comparison)
- Neural networks in TensorFlow and Keras (building, training, fine-tuning, transfer learning, architectures for image and language processing)
- Model productionization – theoretical aspects of monitoring and day-to-day ML operations
