Introduction to Machine Learning Training Course

This instructor-led, live training in India (available online or onsite) is designed for data scientists and software engineers who wish to use AdaBoost to develop boosting algorithms for machine learning with Python.

By the end of this training, participants will be able to:

• Set up the necessary development environment to begin building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This course delves into the application of AI—particularly focusing on Machine Learning and Deep Learning—within the automotive industry. It explores which technologies can be potentially deployed across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.

Understanding the fundamentals of artificial intelligence demonstrates how intelligent technology is transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside communication, perception, and autonomous actions. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led, live training in India (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

Artificial Neural Networks are computational models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as a practical implementation of AI. Furthermore, Deep Learning represents a specialized subset of Machine Learning.

This instructor-led live training in India (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimize chatbot performance over time.

This instructor-led, live training in India (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in India (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

By the end of this training, participants will be able to:

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

This instructor-led, live training in India (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in India (online or on-site) is intended for data scientists, data analysts, and developers looking to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

Upon completion of this training, participants will be able to:

• Explore the AutoML product suite to implement various services for different data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning outcomes.
• Utilize trained models to make predictions that align with business objectives and needs.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Explore the Kubeflow ecosystem and its core components.
• Develop reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Efficiently serve machine learning models via Kubeflow Serving.

Course Format

• Guided presentations coupled with collaborative discussions.
• Hands-on labs utilizing real Kubeflow components.
• Practical exercises to construct end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led live training in India (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training (online or onsite) is designed for developers looking to utilize Google’s ML Kit to build machine learning models that are optimized for mobile device processing.

By the conclusion of this training, participants will be able to:

• Set up the necessary development environment to commence the creation of machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS apps using ML Kit APIs.
• Enhance and optimize existing applications by leveraging the ML Kit SDK for on-device processing and deployment.

This instructor-led live training in India (online or onsite) targets data scientists and software engineers eager to use Random Forest for developing machine learning algorithms designed for large datasets.

By the conclusion of this training, participants will be able to:

• Set up the necessary development environment to commence building machine learning models with Random Forest.
• Understand the advantages of Random Forest and its application in resolving classification and regression issues.
• Learn techniques for handling large datasets and interpreting the complexities of multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance through hyperparameter tuning.

This instructor-led live training in India (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
• Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.