IoT Data Analytics and Machine Learning
Accredited by Averest
350 Learners
INTERMEDIATE
The integration of IoT Data Analytics and Machine Learning enables organizations to harness the power of connected devices to gather vast amounts of data, analyze patterns, and derive actionable insights. This 5-day IoT Data Analytics and Machine Learning training program offers participants a comprehensive understanding of how to apply machine learning techniques to IoT data, improve decision-making, optimize processes, and create intelligent systems. Through theoretical insights and hands-on exercises, participants will learn to develop, deploy, and manage machine learning models for IoT data analytics.
Accreditation With .
IoT Data Analytics and Machine Learning Overview
This comprehensive 5-day training program equips participants with the skills to harness the power of IoT data using machine learning techniques. You'll delve into the fundamentals of IoT data collection, processing, and analysis, while learning to apply advanced machine learning algorithms to extract valuable insights.
What You Will Learn ?
- Understand the principles of IoT data collection, processing, and storage and their impact on machine learning models.
- Learn about key machine learning techniques for analyzing and gaining insights from IoT data.
- Develop machine learning models for IoT applications, such as predictive maintenance and anomaly detection.
- Gain hands-on experience with popular tools and platforms for IoT data analytics and machine learning, such as Python, TensorFlow, and cloud-based analytics platforms.
- Explore real-world use cases of IoT data analytics and machine learning in industries such as manufacturing, healthcare, and smart cities.
Course Key Features
- Flexible Learning Options
- Industry-Relevant Content
- Expert Instructors
- Hands-on Learning
- Comprehensive Coverage
Training Options
In Class
- 5-days in-class training
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- All related Averest's quality control tools & required stationary
- 5 or 4 stars training venue
- Pay later by invoice -OR- at the time of checkout by credit card
- Continuous learner assistance and support
Online
- Continuous learner assistance and support
- 5-days instructor-led training
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- Pay later by invoice -OR- at the time of checkout by credit card
Corporate Training
- A highly customized Corporate Training service designed exclusively for corporate employees and teams. Our training programs are meticulously planned and executed to fill knowledge and experience gaps, helping organizations achieve their business goals. With a comprehensive assessment and tailored curriculum, our experienced trainers deliver modules in areas of accreditation requirements as well as complementary practices such as leadership, communication, and technology adoption. Official certification exam voucher is provided upon completion, ensuring professional growth and measurable results. Contact us now to partner with Averest Training in order to bridge the gaps in your workforce and unlock the full potential of your team.
Schedules
Filters:
2025-May
12 - 16
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-May
19 - 23
London, GB London, United Kingdom
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Jun
09 - 13
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Jun
23 - 27
Istanbul, TR Istanbul, Turkey
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Jul
07 - 11
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Jul
14 - 18
London, GB London, United Kingdom
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Aug
04 - 08
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Aug
11 - 15
Istanbul, TR Istanbul, Turkey
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Sep
01 - 05
London, GB London, United Kingdom
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Sep
08 - 12
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Oct
06 - 10
Istanbul, TR Istanbul, Turkey
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Oct
13 - 17
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Nov
10 - 14
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Nov
17 - 21
London, GB London, United Kingdom
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
2025-Dec
08 - 12
Online
5 Days,
09:00 - 13:00,UTC +03:00,
$ 3500.00
$ 4000.00
2025-Dec
15 - 19
Istanbul, TR Istanbul, Turkey
5 Days,
09:00 - 13:00,UTC +03:00,
$ 4950.00
$ 5200.00
IoT Data Analytics and Machine Learning Training Cirriculum
Eligibility .
This course is designed for Data Scientists and Machine Learning Engineers, IoT Developers and Engineers, Business Leaders and Entrepreneurs, IT and Network Professionals, Tech Enthusiasts.
Pre-requisites .
To ensure a successful learning experience, participants should have a strong foundation in Python programming, data structures, basic statistics, and linear algebra. Additionally, a basic understanding of IoT devices, protocols, and cloud computing is beneficial.
IoT Data Analytics and Machine Learning Course Content .
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Module 1 Introduction to IoT Data Analytics and Machine Learning.- Overview of IoT data: Types of data generated by IoT devices (structured, unstructured, time-series data).
- Data analytics lifecycle in IoT: Collection, preprocessing, analysis, and visualization.
- Introduction to machine learning: Supervised vs. unsupervised learning, classification, regression, and clustering.
- How IoT and machine learning intersect: Using machine learning for predictive analytics, anomaly detection, and optimization in IoT systems.
- Real-world applications of IoT data analytics: Smart cities, healthcare monitoring, industrial automation, and predictive maintenance.
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Module 2 Preprocessing IoT Data for Machine Learning.- IoT data challenges: High volume, noise, missing data, and data from heterogeneous sources.
- Data preprocessing techniques: Cleaning, normalization, feature selection, and transformation.
- Handling time-series data: Techniques for processing temporal IoT data and extracting meaningful features.
- Dimensionality reduction techniques: Principal Component Analysis (PCA) and t-SNE for simplifying large datasets.
- Tools for IoT data preprocessing: Pandas, NumPy, and time-series data libraries.
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Module 3 Machine Learning Models for IoT Data Analytics.- Building and training machine learning models: Linear regression, decision trees, random forests, and k-means clustering.
- Time-series analysis and forecasting for IoT data: ARIMA, LSTM (Long Short-Term Memory) models for prediction.
- Anomaly detection in IoT systems: Techniques for identifying outliers and faults in sensor data.
- Predictive maintenance with machine learning: Using historical IoT data to predict equipment failures.
- Real-time analytics for IoT: Streaming analytics and online learning models.
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Module 4 IoT Data Analytics Platforms and Tools.- IoT analytics platforms: AWS IoT Analytics, Azure IoT Hub, and Google Cloud IoT for large-scale data analysis.
- Deploying machine learning models in IoT systems: Integrating models into IoT architectures for real-time analytics.
- Edge analytics and machine learning: Running models on edge devices to reduce latency and optimize performance.
- Data visualization and dashboards: Using tools like Tableau, Power BI, or Python visualization libraries (e.g., Matplotlib, Seaborn) to present insights from IoT data.
- Cloud-based vs. edge-based machine learning: Deciding when to use cloud computing vs. edge devices for processing IoT data.
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Module 5 Real-World Use Cases and Future Trends in IoT Data Analytics and Machine Learning.- Real-world IoT and machine learning use cases: Predictive maintenance in manufacturing, smart home automation, and healthcare monitoring.
- Advanced IoT analytics: Reinforcement learning, federated learning, and AI-based optimization techniques.
- Scaling machine learning models for large-scale IoT deployments: Handling large datasets and distributed learning.
- Security and privacy challenges: Addressing data privacy, secure data transmission, and compliance with regulations in IoT analytics.
- Future trends in IoT and machine learning: Autonomous IoT systems, AI at the edge, and the rise of 5G-enabled IoT.
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