Implementing Machine Learning - eLearning Bundle Course

Course Details:

Length: 8 courses

Access Length: 6 months

Price: $600/person (USD)

Bulk Pricing: 10+ Contact Us

Course Features:

Instant Access After Purchase

Lecture by Recorded Video

Stop and Start as Needed

Certificate of Completion

Software Lab Included?: No

Delivery Method:

Self-Paced Online

Individuals and Groups
@ Your Location

Course Overview

This eLearning bundle consists of these courses:

Deploying Machine Learning Models with Flask for Beginners
Machine Learning in the Cloud With Azure Machine Learning
Machine Learning Projects with TensorFlow 2.0
Projects in Machine Learning: From Beginner to Professional

Also Included - 4 Courses: An Essential Career Skills Pack with 4 courses in key areas for career management and growth, including Time Management, Digital Skills, Creativity and Soft Skills.

How it Works

This course is a self-paced learning solution to fit your own schedule. Certstaffix Training eLearning courses you take on your own schedule in a web browser.

Learn at your own pace - Start and stop as it is convenient for you. Pick up where you left off.
Lecture utilizing video and recorded screen shots
6 month subscription length
Instant Access After Purchase

Have more than 10 students needing this course? Contact Us for bulk pricing.

Course Notes

This is a lecture only eLearning course. If you wish to practice with hands-on activities, you must provide the software and environment.

Languages:

Audio/Video: American English
Subtitles (Closed Caption): No

Key Features:

Video
Audio Narration

Course Topics

Deploying Machine Learning Models with Flask for Beginners - 2 hrs 31 min

Flask is a web application framework used to develop web applications. Getting started with Flask is easy, and its power lies in its ability to scale up to complex applications. In this course, you’ll learn the most effective ways to use Flask in order to create your own web application.

You’ll begin with an introduction to Flask and quickly dive into defining and training your model. You’ll perform various actions on this model to train it and ensure that it is of the best quality for your application. You’ll also create and test API endpoints so you can predict the model’s behavior over time.

By the end of this course, you’ll have the confidence to deploy this application to the web and learn how to fix any errors that may arise during this process.

Define and train your model
Test the model availability and make predictions
Create a Flask endpoint and perform quality checks
Use image classification in your Flask application
Predict images on the Flask application
Deploy your machine learning model to the web

Machine Learning in the Cloud With Azure Machine Learning - 2 hrs 59 min

With the arrival of cloud computing and multi-core machines, we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of machine learning in your daily life. Machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use web service in the cloud, which could do most of the heavy lifting for us? What if it scaled dynamically based on our data volume and velocity? The answer is the new cloud service from Microsoft called Azure machine learning.

Define Azure machine learning
Identify various machine learning algorithms supported by Azure machine learning
Build and run a machine learning experiment with real-world datasets
Use classification machine learning algorithms
Use regression machine learning algorithms
Expose the Azure ML machine learning experiment as a web service or API
Integrate the Azure ML machine learning experiment API with a web application

Machine Learning Projects with TensorFlow 2.0 - 4 hrs 20 min

TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated APIs.

This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.

Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.

By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.

Build TensorFlow 20 projects by exploring its new features
Analyze the Titanic data set to obtain desired results with ease
Implement and organize your Tensorflow projects in a professional manner
Use Tensorboard to inspect various metrics and monitor your project’s performance
Research and make the most of other people's Kaggle solutions
Use OpenAI Gym Environments for implementing state of the art reinforcement learning techniques using TF-Agents
Apply the latest Transfer Learning techniques from Tensorflow

Projects in Machine Learning: From Beginner to Professional - 15 hrs 26 min

From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.

The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model. By the end of this course, you will have gained the skills to create real-world ML solutions.

All the recourses for this course are avialable at The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.

By the end of this course, you will have gained the skills to create real-world ML solutions.

Detect credit card fraud by using probability densities
Identify natural language processing methodology
Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network
Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)
Solve a text classification task using multiple classification algorithms
Use K-means clustering in an unsupervised algorithm

Essential Career Skills Pack:

Productivity and Time Management - 30 minutes

It seems that there is never enough time in the day. But, since we all get the same 24 hours, why is it that some people achieve so much more with their time than others? This course will explain how to plan and prioritize tasks, so that we can make the most of the limited time we have. By using the time-management techniques in this course, you can improve your ability to function more effectively – even when time is tight and pressures are high. So, by the end of the course you will have the knowledge, skills and confidence to be an effective manager of your time.

Basic Digital Skills - 13 minutes

With the rise of digital transformation and technology, having a basic digital literacy is essential for all types of jobs, regardless of the industry. To stay competitive and be successful in the workplace, enhancing your digital skills should be a top priority.

4 Ways to Boost Creativity - 30 minutes

The digital economy is opening up ways for everyone to be creative. It doesn’t just mean being artistic – it’s more about ideas, solutions, alternatives, incremental improvements. Peter Quarry and Eve Ash discuss ways that mental capacity can be developed, perspectives changed, group power leveraged and making things actually happen.

The 11 Essential Career Soft Skills - 1 hour 10 minutes

Soft Skills are the traits, characteristics, habits, and skills needed to survive and thrive in the modern work world. Soft skills aren't usually taught in school, but you will learn them all here in this course. Are you someone that other people in your organization and industry like to work with, collaborate with and partner with? Are you seen as a valuable asset to any new project that comes along?

This soft skills training course will teach you how to develop the skills that can make the difference between a lackluster career that tops out at middle management versus one that lands you in the executive suite. Or to wherever you define career success. So many soft skills seem like common sense at first glance, but they are not commonly applied by most workers. This soft skills training course will give you an edge over your competitors. It will also make your job, your career and your life more rewarding and enjoyable.

Explore Machine Learning Training Classes Near Me:

Certstaffix Training offers self-paced eLearning courses for Machine Learning, ideal for those looking for convenient and flexible learning options. With these online classes, you can save time trekking to and from a physical class location by taking courses remotely. Have the ability to learn when it's most convenient for you with our eLearning courses – no more worrying about searching for "Machine Learning classes near me" and commuting long distances. Take advantage of our online Machine Learning classes today to get the education you need quickly. Start learning today and see how Certstaffix Training can help you reach your goals.

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Course FAQs

What is the Class Format?

This training is a self-paced eLearning course that you have access to for 6 months after purchase.

What Is Machine Learning With Flask?

Machine learning with Flask is a powerful tool that allows users to easily incorporate predictive capabilities into their web applications. With the help of machine learning, users can define and train models, test model availability and make predictions, create Flask endpoints to perform quality checks, use image classification in their Flask applications, predict images on the Flask application, and deploy their machine learning model to the web. This makes it much easier for developers to create powerful applications with advanced prediction capabilities.

Using machine learning with Flask also increases the accuracy of predictions, allowing users to rely on more accurate results and improved performance in their applications. By incorporating machine learning into your applications, you can create a smarter, more efficient experience for users. Flask enables easy scalability to meet the changing needs of your application over time.

Machine learning with Flask is an incredibly useful tool that allows developers to create smarter applications and deliver better results for their users. By leveraging advanced predictive capabilities and increased accuracy, developers have more power at their fingertips when creating web applications. Machine learning with Flask is easy to use and offers powerful scalability to fit the changing needs of your applications as they evolve naturally.

What Is Machine Learning With Microsoft Azure?

Microsoft Azure Machine Learning is a cloud-based service that makes it easy to develop and deploy powerful machine learning models. It provides various algorithms and services that can be used to build, train, and publish predictive analytics solutions. With Microsoft Azure Machine Learning, data scientists and developers can easily prepare datasets for modeling, create custom machine learning models, evaluate model performance, and develop web-based applications that can consume the models.

Microsoft Azure Machine Learning supports various machine learning algorithms, including classification algorithms such as logistic regression, decision tree, random forest, and support vector machines (SVM); clustering algorithms such as k-means; and regression algorithms such as ordinary least squares (OLS) regression. It also offers services such as feature engineering, automated machine learning, and neural networks.

Using Microsoft Azure Machine Learning, data scientists and developers can build and run experiments using real-world datasets to create predictive analytics solutions. The service provides an easy-to-use interface for creating data pipelines, processing the data, and creating the models.

Once the machine learning experiment is complete, it can be exposed as a web service or API to integrate with web applications. This allows developers to easily put their predictive analytics solutions into production without having to write any additional code.

Microsoft Azure Machine Learning provides powerful tools for data scientists and developers to build and deploy machine learning models quickly and easily. With its support for various algorithms and services, it is well-suited for building a wide range of predictive analytics solutions.

What Is Machine Learning With TensorFlow?

TensorFlow is a powerful open-source software library for numerical computation and machine learning. It has become the de facto standard when it comes to building deep neural networks with many layers. With TensorFlow, you can easily build, deploy, and manage large-scale machine learning applications in both enterprise and cloud environments.

Using the latest features of TensorFlow, you can use it to create projects from scratch or perform complex tasks such as text summarization and computer vision. You can also use the library for efficient data science workflows with the help of its API libraries.

Using Tensorboard, you can monitor various metrics and inspect your project's performance in real-time. This makes it easier to identify areas for improvement and optimize your models.

TensorFlow also provides built-in support for OpenAI Gym environments, allowing you to implement state-of-the-art reinforcement learning techniques using TF Agents. Transfer learning can be used to quickly train deep neural networks on a variety of tasks. You can research and make the most of other people's Kaggle solutions to quickly get started on your projects.

TensorFlow is a powerful tool for anyone looking to build machine learning applications and develop their skills in this exciting field. With its extensive feature set, you can easily create complex models, experiment with various techniques, and innovate with cutting-edge technology. Whether you are a beginner or an experienced expert, TensorFlow provides the tools to help you build and manage machine learning projects with ease.

What Are Some Example Machine Learning Projects?

Machine Learning projects can provide several valuable benefits to businesses. For example, machine learning algorithms can be used to detect credit card fraud by using probability densities. Natural Language Processing (NLP) methodology can be utilized to better understand customer conversations and sentiments. Datasets can also be used to implement a deep neural network that can improve image quality. Text classification tasks can be solved using multiple algorithms, such as K-means clustering in unsupervised algorithms.

Businesses can take advantage of machine learning projects to automate processes and gain valuable insights into data. By leveraging the power of machine learning, businesses can improve efficiency, accuracy, and speed. These projects have the potential to improve customer experiences while saving time and money in a variety of industries.

Related Machine Learning Information:

How Much Do Machine Learning Training Courses Cost?

Self-Paced Machine Learning eLearning courses cost $475 at the starting point per student. Group purchase discounts are available.

What Machine Learning Skills Should I Learn?

A: If you are wondering what Machine Learning skills are important to learn, we've written a Machine Learning Skills and Learning Guide that maps out Machine Learning skills that are key to master and which of our courses teaches each skill.

Read Our Machine Learning Skills and Learning Guide

How do you get training in machine learning?

A: There are a few ways to get training in machine learning. One way is to take an online Machine Learning course. There are many courses available, and they vary in difficulty and cost. You can also find free courses offered by universities and other institutions. Another way to get training is to attend a workshop or conference. These events are typically organized by companies or research organizations, and they offer the opportunity to learn from experts in the field. Finally, you can read books or articles on machine learning. This is a good option if you want to learn at your own pace and choose your own resources.

What is best way to learn machine learning?

A: There is no one-size-fits-all answer to this question, as the best way to learn machine learning depends on your specific needs and goals. However, some general tips that may help include: attending online or in-person training courses, practicing with online tutorials or coding challenges, and reading articles or books on the subject. Whatever approach you take, make sure to set aside enough time to practice and master the concepts before moving on to more difficult material.

Can I learn machine learning without coding?

A: Yes, you can learn machine learning without coding. There are many resources available that can help you understand the basics of machine learning. However, if you want to be able to apply machine learning algorithms, it is important to have at least a basic understanding of coding. Coding will allow you to implement machine learning algorithms and test them on data sets. Without coding, you would not be able to fully utilize the power of machine learning.

What are the top Machine Learning skills?

A: There is no doubt that machine learning is one of the most in-demand skills today. With the vast amounts of data being generated every day, businesses are turning to machine learning to help them make sense of it all and extract valuable insights.

But what exactly are the top machine learning skills that employers are looking for? Here’s a look at some of the most sought-after skills in this field:

Top Machine Learning Skills

1. Data Wrangling

Before you can even begin to apply machine learning algorithms to data, you need to be able to clean and prepare that data first. This process, known as data wrangling, involves everything from dealing with missing values and outliers to normalizing data and creating features.

2. Linear Algebra

Linear algebra is a fundamental mathematical discipline that forms the basis of many machine learning algorithms. It’s used for everything from solving systems of linear equations to performing matrix operations and decompositions.

3. Probability and Statistics

Probability and statistics are also essential for machine learning. They’re used for tasks such as estimating model parameters, assessing model performance, and detecting patterns in data.

4. Programming

Of course, you can’t do machine learning without being able to code. While there are some specialized machine learning languages out there, most practitioners use general-purpose programming languages like Python or R for their work.

5. Machine Learning Algorithms

Last but not least, you need to be familiar with the various machine learning algorithms that are available. These include popular methods such as decision trees, support vector machines, and neural networks.

If you’re looking to get started in machine learning, then these are some of the skills you need to focus on. By honing these skills, you’ll be well-positioned to pursue a successful career in this exciting field.