Machine Learning With Python
Instructor-led training in Machine Learning With Python. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This course provides a comprehensive introduction to machine learning using Python, covering essential algorithms and techniques for building predictive models. Participants will learn how to leverage Python's powerful libraries to analyze data and implement machine learning workflows effectively.
What you'll learn
- You will be able to implement machine learning algorithms using Python libraries such as Scikit-learn and TensorFlow.
- You will be able to preprocess and clean data for better model performance.
- You will be able to evaluate model performance using various metrics and techniques.
- You will be able to select appropriate models based on data characteristics and business requirements.
- You will be able to visualize data and model outputs using Python's visualization libraries.
- You will be able to deploy machine learning models into production environments.
- You will be able to troubleshoot common issues in machine learning projects.
- You will be able to apply machine learning techniques to real-world datasets.
Curriculum
8 modules · outline is indicative and can be tailored to your team.
1Introduction to Machine Learning
- Overview of machine learning concepts
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Understanding the machine learning workflow
2Data Preprocessing
- Data cleaning techniques
- Handling missing values
- Feature scaling and normalization
3Supervised Learning Algorithms
- Linear regression and logistic regression
- Decision trees and random forests
- Support vector machines
4Unsupervised Learning Algorithms
- Clustering techniques: K-means and hierarchical clustering
- Dimensionality reduction: PCA and t-SNE
- Association rule learning
5Model Evaluation and Selection
- Cross-validation techniques
- Performance metrics: accuracy, precision, recall, F1 score
- Hyperparameter tuning
6Model Deployment
- Introduction to model deployment strategies
- Using Flask for web applications
- Best practices for deploying machine learning models
7Data Visualization
- Introduction to Matplotlib and Seaborn
- Creating informative visualizations
- Visualizing model performance
8Real-world Applications
- Case studies of machine learning in various industries
- Ethics in machine learning
- Future trends in machine learning
Prerequisites
Some programming experience in Python is recommended.
Who should attend
This course is ideal for data analysts, data scientists, and professionals looking to enhance their machine learning skills.
Certification
Frequently asked questions
What is the delivery format of the course?
The course is delivered via live online sessions with interactive components.
How long is the course?
The course spans 8 weeks, with one session each week lasting 2 hours.
Will I receive a certificate upon completion?
Yes, participants will receive an Skilvi course-completion certificate.
Are exam vouchers included?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Some programming experience in Python is recommended.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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