Emerging technologies training at Bodhih helps teams build practical skills in machine learning with Python, deep learning, generative AI foundations and the Internet of Things. Programs combine core concepts with hands-on labs and real-world case studies, and are customised for corporate data, engineering and IoT teams. They are delivered in person or live virtual.
- Standard duration: 5-day, customisable to your needs
- Delivered in person at your location or as live virtual sessions
- Designed with the ADDIE model and evaluated for behaviour change
- Offered by Bodhih since 2008 to 2,000+ organisations across 7 regions
Where and how: emerging technologies training as an in-person workshop in Bengaluru, Mumbai, Delhi NCR, Gurugram, Hyderabad, Chennai, Pune, Kolkata, Ahmedabad and Jaipur; as a live online course; or delivered overseas in Dubai, Singapore and across the Middle East, Asia and Africa.
Machine Learning with Python
Program Overview:
- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- Machine Learning with Python program provides deeper understanding of the Machine Learning concepts, algorithms and its implementation.
- Covers Supervised and Unsupervised learning algorithms.
- Coverage of Deep Learning Concepts
- Data Science and its concepts
- A crash course on Python
- Case Studies and Real time Projects.
Training Duration:
5-day Instructor Led Training
Target Audience:
- Data Analyst
- Developers
- Data Architects
Training Prerequisite:
Basic Python Programming Knowledge.
Training Outcome:
- Practical Approach
- Hands on training session
- Real time project and case studies
- 24/7 support
IoT (From Beginner to Expert )
Program Overview:
IoT systems allow users to achieve deeper automation, analysis, and integration within a system. They improve the reach of these areas and their accuracy. IoT utilizes existing and emerging technology for sensing, networking, and robotics.
IoT is an advanced automation and analytics system. It makes use of Artificial Intelligence and Big Data to deliver complete systems. These systems enable transparency, when applied to a network.
Targeted Audience:
Audience working on the IoT Solutions.
Training Duration:
5 days Instructor Led Training.
Training Prerequisite:
Good Understanding of Internet, Networking and Cloud solutions.
Training Outcome:
- Practical Approach
- Hands on training session
- Real time project and case studies
- 24/7 support
Solutions & Services
What participants will be able to do
Prepare data for machine learning
Participants clean, explore and engineer features using pandas, NumPy and visualisation libraries.
Build and evaluate models
Developers train supervised and unsupervised models with scikit-learn and judge them with the right metrics.
Understand deep learning
Participants build simple neural networks and understand how modern AI models, including large language models, work.
Apply generative AI responsibly
Teams understand prompting, retrieval-augmented generation and the risks of bias, privacy and hallucination.
Design IoT systems
Engineers connect sensors and devices to the cloud using common IoT protocols.
Plan practical use cases
Teams identify where ML, AI or IoT can add value in their own processes.
Who should attend
- Data analysts moving into machine learning
- Developers adding AI features to applications
- Data architects designing ML and IoT data pipelines
- Engineers building or supporting IoT solutions
- Technical leads assessing AI and IoT opportunities
Recommended program outline
Recommended design, customised to your context after a short needs analysis.
01Python for data scienceModule 1 · Machine learning program, Day 1+
- A crash course on Python, notebooks and core libraries
- Data wrangling with pandas and NumPy
- Exploratory analysis and visualisation
02Supervised and unsupervised learningModule 2 · Machine learning program, Days 2 and 3+
- Regression, classification, decision trees and ensembles
- Clustering and dimensionality reduction
- Train-test splits, cross-validation and evaluation metrics
- Avoiding overfitting and data leakage
03Deep learning and generative AI foundationsModule 3 · Machine learning program, Day 4+
- Neural networks and training basics with a common framework
- How transformers and large language models work, in overview
- Prompting, embeddings and retrieval-augmented generation
- Responsible AI: bias, privacy and evaluation
04ML capstone and deployment basicsModule 4 · Machine learning program, Day 5+
- End-to-end project from data to model
- Saving models and serving predictions via an API
- MLOps concepts: versioning and monitoring
05IoT architecture and devicesModule 5 · IoT program, Days 1 and 2+
- Sensors, actuators, microcontrollers and gateways
- Edge versus cloud processing
- Hands-on work with a development board
06IoT connectivity, cloud and securityModule 6 · IoT program, Days 3 to 5+
- Protocols such as MQTT and HTTP, and wireless options
- Sending device data to a cloud IoT platform and dashboards
- IoT security basics and a capstone solution
When to choose this
Frequently asked questions
What is machine learning with Python?
Machine learning with Python means using Python and its libraries, such as pandas, NumPy and scikit-learn, to build models that learn patterns from data and make predictions. Python is widely used for this because of its readable syntax and large ecosystem. Bodhih's program takes participants from data preparation to building and evaluating models.
Does the program cover generative AI and large language models?
Yes, at a foundation level. After covering classical machine learning and neural networks, participants learn how large language models work in overview, how to prompt them effectively and how retrieval-augmented generation connects them to company data. Responsible use, including privacy and accuracy risks, is covered. Deeper generative AI programs can be customised.
What is the difference between AI, machine learning and deep learning?
Artificial intelligence is the broad goal of making systems perform tasks that need human-like intelligence. Machine learning is a way of achieving it by learning from data rather than following hand-written rules. Deep learning is a type of machine learning that uses multi-layer neural networks, and it powers most modern image, speech and language models.
What do you learn in IoT training?
IoT training covers how connected systems are built, from sensors and microcontrollers to gateways, networks and cloud platforms. Participants learn common protocols such as MQTT, work with a development board, send data to the cloud and build a simple dashboard. Security is covered throughout, because connected devices can be an easy target.
What are the prerequisites for machine learning training?
Basic Python programming knowledge is required, and comfort with school-level statistics and algebra helps. The first day includes a Python crash course focused on data work. For IoT, participants need a good understanding of the internet, networking and cloud basics. Bodhih can adjust depth after a short needs analysis.
Who is the Emerging Technologies program for?
Data Analyst, Developers, Data Architects. Bodhih tailors the examples, case studies and depth to your industry, culture and team level.
How long is the Emerging Technologies program?
The standard program runs 5-day. Bodhih can shorten or extend it to fit your goals and schedule.
Can Emerging Technologies be delivered online?
Yes. Emerging Technologies can run in person at your location, as live virtual sessions for distributed or hybrid teams, or as a blended journey that adds self-paced learning on Bodhih.org between sessions.
How is the impact of Emerging Technologies measured?
Success measures for Emerging Technologies are agreed with you before design, as part of the ADDIE model. Bodhih can add pre- and post-program assessments on AssessAll and manager check-ins to track behaviour change.