Corporate Machine Learning with TensorFlow Training Course

Name: Machine Learning with TensorFlow Training
Availability: InStock

Edstellar's instructor-led Machine Learning with TensorFlow training course encapsulates the essence of utilizing TensorFlow, a leading machine learning library, to create, train, and deploy machine learning models efficiently. The course equips teams with the skills to develop innovative solutions and stay competitive in data-driven terrain.

Duration

16 - 24 hrs

Delivery Type

Instructor-led Group Training
(Virtual / On-site / Off-site)

Training Available in

10 Languages

Multiple Locations

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About Learning Outcomes Key Benefits Course Outline Target Audience Training Modes Certificate Trainers Request a Training Quote

Drive Team Excellence with Machine Learning with TensorFlow Corporate Training

Machine Learning with TensorFlow represents a transformative approach to artificial intelligence, enabling computers to learn from and make data-based decisions. The technology is pivotal for organizations as it empowers teams to leverage TensorFlow for advanced machine learning applications, driving efficiency, innovation, and strategic decision-making. The need for a Machine Learning with TensorFlow training course is paramount, as it equips professionals with the knowledge to harness the power of AI effectively.

Edstellar's instructor-led Machine Learning with TensorFlow training course stands out with its practical, hands-on approach, led by industry experts. The training provides theoretical learning for professionals to apply what is learned in realistic scenarios. The curriculum is designed to be customizable, ensuring relevance to the organization's unique challenges and requirements. The course delivered through virtual/onsite training modes provides an immersive, practical learning experience, facilitating a deep understanding of Machine Learning with TensorFlow and its application in business contexts.

Get Customized Expert-led Training for Your Teams

Customized Training Delivery

Scale Your Training: Small to Large Teams

In-person Onsite, Live Virtual or Hybrid Training Modes

Plan from 2000+ Industry-ready Training Programs

Experience Hands-On Learning from Industry Experts

Delivery Capability Across 100+ Countries & 10+ Languages

Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

Neural Networks
Neural Networks are computational models inspired by the human brain, crucial for roles in AI and data science. This skill is important for developing advanced algorithms, enhancing predictive analytics, and driving innovation in machine learning applications.
TensorFlow Framework
TensorFlow Framework is an open-source library for machine learning and deep learning. This skill is important for data scientists and AI engineers to build, train, and deploy models efficiently.
Model Building
Model Building is the process of creating mathematical representations of real-world systems. this skill is important for data analysts and data scientists as it enables accurate predictions and informed decision-making.
Model Optimization
Model Optimization is the process of refining machine learning models to enhance performance and efficiency. This skill is important for data scientists and AI engineers, as it ensures accurate predictions and resource-efficient solutions.
Convolutional Neural Networks
Convolutional Neural Networks are deep learning models designed for image processing. This skill is important for roles in AI, computer vision, and data analysis, enabling advanced visual recognition tasks.
Natural Language Processing
Natural Language Processing is the AI-driven ability to analyze and interpret human language. This skill is important for roles in data science, AI development, and linguistics, enabling effective communication and insights from text data.

What Your Team Will Achieve After This Training

Equip professionals with the capability to preprocess and transform data, ensuring it is in the optimal format for analysis and model training
Develop skills to design and implement neural networks using TensorFlow, enabling the creation of sophisticated AI models that can solve complex problems in various business sectors
Apply advanced optimization techniques to improve the performance and efficiency of machine learning models, ensuring professionals can handle large datasets required in professional settings
Explore the application of convolutional and recurrent neural networks for advanced tasks, contributing to the development of intelligent systems that enhance customer experiences and automate tasks
Learn to leverage TensorFlow's comprehensive ecosystem to build and deploy machine learning models effectively in professional environments, enhancing product innovation and operational efficiency

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

Machine Learning and Neural Networks Overview

What is machine learning?
- Definition and history
- Key concepts and terminologies
- Types of machine learning models
Machine learning problems and applications
- Classification, regression, and clustering problems
- Impact of machine learning on society
Neural networks overview
- Basics of neural networks
- Structure and functioning of neurons
- Layers in neural networks
Python machine learning ecosystem: TensorFlow vs scikit-learn
- Overview of TensorFlow and scikit-learn
- Key differences and use cases
- Choosing the right tool for the task

Machine Learning Basics

Learning and predicting
- The learning process explained
- Making predictions with trained models
Supervised vs unsupervised learning
- Differences and applications
- Examples of both learning types
Feature engineering, feature selection, feature scaling
- Importance of feature engineering
- Techniques for feature selection and scaling
Training data and test data
- Importance of data splitting
Cross-validation
- Understanding cross-validation
- Different cross-validation techniques
Evaluation metrics
- Common evaluation metrics for classification and regression
- Understanding confusion matrix, precision, recall, and F1 score

Loading, Exploring, and Pre-Processing Data

The tf.data API
- Introduction to tf.data
- Creating datasets and data pipelines
Working with NumPy arrays
- NumPy arrays in data preprocessing
- Integration of NumPy with TensorFlow
TensorFlow datasets
- Exploring TensorFlow datasets
- Loading and preprocessing datasets

Building the First Neural Network Model

Choosing a network architecture
- Criteria for choosing the right architecture
- Overview of common architectures
The tf.keras API
- Introduction to Keras in TensorFlow
- Building models with Keras
Setting up and compiling a model in TensorFlow
- Model configuration
- Compilation options and optimizers
Training the model
- Training processes and techniques
- Monitoring training progress
Model evaluation
- Evaluating model performance
- Understanding evaluation metrics
Making predictions
- Generating predictions with trained models
- Interpreting prediction results

TensorFlow Demos

Classification: Predicting a label
- Steps in building a classification model
- Demo: Simple classification task
Text classification with TensorFlow
- Text preprocessing techniques
- Building and training a text classification model
Image classification with TensorFlow
- Image data preparation
- Constructing an image classification model
Regression: Predicting a quantity
- Basics of regression analysis
- Implementing a regression model in TensorFlow
Linear regression with TensorFlow
- Linear regression fundamentals
- Building a linear regression model
Improving prediction quality
- Techniques to enhance model predictions
- Overfitting and underfitting
Error analysis
- Identifying and analyzing model errors
- Strategies for error reduction
Hyper-parameter tuning
- Importance of hyper-parameter tuning
- Methods for hyper-parameter optimization

Who Should Attend?

This program suits professionals at many levels across the organization, including:

Data Scientists
Machine Learning Engineers
Marketing Assistants
Data Analysts
Software Engineers
Research Scientists
IT Specialists
Data Engineers
Algorithm Engineers
Computer Vision Engineers
Big Data Specialists
Managers

What are the Prerequisites?

Professionals with a basic understanding of Python programming and fundamental machine learning concepts, experience developing with languages such as C++, C#, Scala and Javascript, and familiarity with Linear Algebra and Vector Calculus can take up the Machine Learning with TensorFlow training course.

Request a Quote for your Corporate Training Requirements

Delivering Training for Organizations across 100 Countries and 10+ Languages

Choose the Format That Fits Your Team

We design training your teams actually engage with, and deliver it the way that suits you best. Through a vetted global trainer network, Edstellar runs sessions in 10+ languages with consistent quality anywhere.

Virtual Machine Learning with TensorFlow Training

Virtual / online: expert-led live sessions delivered anywhere, with consistency and easy scheduling.

We deliver anywhere worldwide

Standardized content for consistent outcomes

Join from own workspace, no travel

We scale to large groups across sites

Interactive tools keep remote learners engaged

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On-site Machine Learning with TensorFlow Training

On-site (in-house): immersive, instructor-led learning at your office.

Our trainers run face-to-face at your office

We tailor setup/content to your workplace and tools

Group exercises drive collaboration

Live demos + hands-on practice

Direct trainer access to clarify doubts

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Off-site Machine Learning with TensorFlow Training

Off-site: focused, instructor-led group learning away from everyday workplace distractions.

We host your teams at a venue of your preferred choice

Built-in group activities for bonding

Full uninterrupted schedule for focus/retention

Boosts morale and signals commitment

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What Sets Edstellar Apart

Experienced Trainers

Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.

Proven Quality

With a strong global track record, Edstellar is known for quality and engaging delivery.

Industry-Relevant Curriculum

Our programs are built by experts to match the demands of today's industry.

Fully Customizable

Every program can be tailored to your organization's goals.

Comprehensive Support

We provide pre- and post-session support for a complete learning experience.

Global Multi-Location & Multilingual Training Delivery

We deliver in multiple languages to support diverse global teams.

Hear from Organizations We've Trained

"This Machine Learning with TensorFlow course was exactly what I needed to advance my career. As a Principal Data Architect, mastering industry best practices has become crucial for my success. The in-depth coverage frameworks I use daily. My ability to architect solutions and solve complex problems has improved substantially. The real-world examples and deep dive into practical simulations were particularly valuable for my professional growth.”

Michelle Bradley

Principal Data Architect,

AI Solutions Platform Provider

"The Machine Learning with TensorFlow training provided critical insights into strategic frameworks that enhanced my consulting capabilities. As a Lead Data Warehouse Engineer, I now leverage real-world case studies exercises on hands-on exercises prepared me perfectly for real-world client scenarios. Client engagement and retention metrics have improved significantly across our practice, demonstrating immediate value from this investment.”

Milan Jovanovic

Lead Data Warehouse Engineer,

ML Model Development Platform

"As a Principal Big Data Engineer leading strategic implementation operations, the Machine Learning with TensorFlow training provided our team with essential advanced methodologies expertise at scale. The complete operational footprint. We completed our comprehensive digital transformation initiative significantly ahead of schedule. This course has proven invaluable for driving our organizational transformation and sustained excellence.”

Anand Chary

Principal Big Data Engineer,

Cognitive Computing Solutions Provider

“Edstellar’s IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We’re excited to extend more of these impactful programs to other business units.”

Aditi Rao

L&D Head,

A Global Technology Company

Recognition That Motivates Your Team

Upon successful completion of the training course offered by Edstellar, employees receive a course completion certificate, symbolizing their dedication to ongoing learning and professional development.

This certificate validates the employee's acquired skills and is a powerful motivator, inspiring them to enhance their expertise further and contribute effectively to organizational success.

Corporate Machine Learning with TensorFlow Training Course

Drive Team Excellence with Machine Learning with TensorFlow Corporate Training

Skills Your Employees Will Gain

What Your Team Will Achieve After This Training

Topics & Program Outline

Machine Learning and Neural Networks Overview

Machine Learning Basics

Loading, Exploring, and Pre-Processing Data

Building the First Neural Network Model

TensorFlow Demos

Who Should Attend?

What are the Prerequisites?

Choose the Format That Fits Your Team

Get a Proposal Shaped to Your Needs

What Sets Edstellar Apart

Experienced Trainers

Proven Quality

Industry-Relevant Curriculum

Fully Customizable

Comprehensive Support

Global Multi-Location & Multilingual Training Delivery

Hear from Organizations We've Trained

Recognition That Motivates Your Team

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