Fraud Detection with Python and TensorFlow Training Course

This intensive, hands-on program delves into advanced Python techniques, engineering best practices, and widely adopted design patterns to help you build maintainable, testable, and high-performance Python applications. The curriculum emphasizes modern tooling, type hints, concurrency models, architectural patterns, and deployment-ready workflows.

This instructor-led live training (available online or onsite) is tailored for intermediate to advanced Python developers looking to adopt professional practices and patterns for production-grade Python systems.

Upon completing this training, participants will be equipped to:

• Utilize Python typing, dataclasses, and type-checking to enhance code reliability.
• Apply design patterns and architectural principles to structure robust applications.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Write well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containers.

Course Format

• Interactive lectures and concise demonstrations.
• Practical labs and coding exercises each day.
• A capstone mini-project that integrates patterns, testing, and deployment.

Customization Options

• For customized training or specific focus areas (such as data, web, or infrastructure), please contact us to arrange.

This course imparts practical engineering methodologies for designing, constructing, testing, and deploying autonomous agentic systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production-grade considerations.

Delivered as instructor-led live training (available online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers aiming to build robust, production-ready autonomous agents using Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to enhance agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and ensure agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for building agents using Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Course Customization Options

• To request a customized training session for this course, please contact us to arrange it.

This course explores practical approaches to Data Science and AI using Python. It equips professionals with the skills to explore data, build machine learning models, and deploy AI-driven applications in business contexts. It covers CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data with Spark, and data-driven storytelling. This course is ideal for beginners seeking a Python data science certification and career-ready analytics training.

Artificial Intelligence with Python focuses on building intelligent systems by leveraging Python’s comprehensive ecosystem of AI and machine learning libraries.

This instructor-led live training, available online or onsite, is designed for intermediate Python developers who want to design, implement, and deploy AI solutions using Python.

Upon completing this training, participants will be able to:

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in India (online or onsite) is designed for business analysts who wish to automate trading using algorithmic trading, Python, and R.

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

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

The course 'Applied AI from Scratch in Python' empowers programmers and data analysts by providing the essential techniques required to construct machine learning solutions entirely from the ground up using Python. It delves into the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection. Additionally, the course explores advanced neural network architectures. Learners will examine proven methodologies for utilizing tools like scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program enables professionals to implement practical ML models, assess algorithmic limitations, and execute applied projects designed for real-world problem-solving.

This instructor-led, live training in India (online or onsite) is aimed at intermediate-level Python developers who wish to enhance their Python development experience using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Understand the AWS Cloud9 IDE interface and features.
• Write, debug, and deploy Python applications in AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployments.

This instructor-led, live training in India (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in India (online or onsite) is designed for data scientists and software engineers who want to leverage Dask within the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the necessary environment to begin building big data processing workflows with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (NumPy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in India (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led live training, conducted in India (either online or onsite), targets intermediate-level data scientists and developers who aim to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led live training, offered online or on-site, is designed for developers who wish to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

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

• Establish the necessary development environment integrating FastAPI, React, and MongoDB.
• Comprehend the essential concepts, features, and benefits associated with the FARM stack.
• Master the process of building REST APIs with FastAPI.
• Learn techniques for designing interactive applications using React.
• Develop, test, and deploy full-stack applications (frontend and backend) leveraging the FARM stack.

This instructor-led live training in India (online or onsite) is designed for developers who want to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater ease and speed.

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

• Set up the essential development environment for building APIs with Python and FastAPI.
• Produce APIs faster and more efficiently using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI.
• Link APIs to a database using SQLAlchemy.
• Apply security and authentication features to APIs using FastAPI utilities.
• Construct container images and deploy web APIs onto a cloud server.

This instructor-led, live training in India (online or on-site) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course starts by providing conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

Part 3 (40%) of the training would be extensively based on TensorFlow - the API of Google's open-source software library for Deep Learning. The examples and hands-on sessions will all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production-like training models, building graphs and logging