Machine Learning With Data Science
Instructor-led training in Machine Learning With Data Science. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This course, Machine Learning With Data Science, provides a comprehensive understanding of machine learning techniques and their applications in data science. You will learn to harness the power of data to create predictive models and gain insights that drive decision-making in various industries.
What you'll learn
- You will be able to identify and apply various machine learning algorithms.
- You will be able to preprocess and clean datasets for optimal model performance.
- You will be able to evaluate model performance using appropriate metrics.
- You will be able to implement supervised and unsupervised learning techniques.
- You will be able to use popular libraries such as Scikit-learn and TensorFlow.
- You will be able to visualize data and model results effectively.
- You will be able to deploy machine learning models into production environments.
- You will be able to communicate insights and findings to stakeholders.
Curriculum
8 modules · outline is indicative and can be tailored to your team.
1Introduction to Machine Learning
- Overview of machine learning and its importance
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Key concepts and terminology
2Data Preparation and Preprocessing
- Data collection and integration techniques
- Handling missing values and outliers
- Feature scaling and encoding categorical variables
3Exploratory Data Analysis (EDA)
- Techniques for visualizing and summarizing data
- Identifying patterns and trends in data
- Using statistical methods for data analysis
4Supervised Learning Algorithms
- Regression techniques: linear regression, decision trees, and more
- Classification techniques: logistic regression, SVM, and random forests
- Model training, testing, and validation
5Unsupervised Learning Techniques
- Clustering methods: K-means, hierarchical clustering
- Dimensionality reduction techniques: PCA and t-SNE
- Applications of unsupervised learning
6Model Evaluation and Optimization
- Understanding evaluation metrics: accuracy, precision, recall, F1-score
- Hyperparameter tuning and model optimization
- Cross-validation techniques
7Deployment and Productionization
- Best practices for deploying machine learning models
- Introduction to model monitoring and maintenance
- Integration with web services and applications
8Communicating Insights
- Techniques for presenting data-driven insights
- Creating impactful visualizations
- Engaging stakeholders with storytelling
Prerequisites
Basic understanding of programming and statistics is recommended.
Who should attend
This course is ideal for data analysts, aspiring data scientists, and professionals looking to enhance their machine learning skills.
Certification
Frequently asked questions
What is the format of the course delivery?
The course is instructor-led and delivered live online.
How long is the course?
The course duration is 8 weeks, with sessions held twice a week.
Will I receive a certificate upon completion?
Yes, you will receive an Skilvi course-completion certificate.
Are exam vouchers provided?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Basic understanding of programming and statistics is recommended.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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