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
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Python programmers looking to expand their competencies in data analysis and machine learning
Training Program
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Computational and Algorithmic Tools (Pandas, NumPy, SciPy)
- Data acquisition
- Data analysis and built-in functions
- Data operations – handling missing data
- Data cleaning procedures
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Visualization (Matplotlib, Seaborn)
- Data visualization and presentation methods
- Exporting and saving visualizations
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Working with APIs and Databases
- Connecting to APIs (as technically feasible)
- Working with relational databases
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Machine Learning in Python (Scikit-learn)
- Model creation and training
- Hyperparameter tuning
- Classification and regression problems
- Clustering and model comparison
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Deep Learning in Python (TensorFlow & Keras)
- Building and training neural networks
- Fine-tuning and transfer learning
- Architectures for image and language processing
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Model Productionization
- Theoretical aspects of deploying ML models
- Monitoring models in production
- Day-to-day machine learning operations (MLOps basics)
