Implementing Machine Learning - eLearning Bundle Course

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.