What Does The Course Offer?

 This course will offer you hands-on knowledge of the fundamentals of data science. You will learn to build your first artificial neural network with the help of deep learning techniques.

We will take logistic regression as a building block and build non-linear neural networks straight out of the gate with Numpy and Python. The best part is that all the materials for this course are free of cost.

Our multiple classes are an extension of the previous binary classification model using the softmax function. With the help of the first principles, we derive the essential training method “backpropagation.” Your course will begin with learning how to code backpropagation in Numpy “the slow way”, and then gradually, we will move to “the fast way” with Numpy features.

After that, we implement a neural network with Google’s new TensorFlow library. If you want to become a pro at machine learning and data science, you should definitely consider enrolling in this course.

Our curriculum is not just limited to basic models like logistic regression and linear regression. Instead, we go above and beyond to facilitate you and enlighten you with the deep knowledge of data sciences.

This course promises you tons of practical examples so you can implement what you learn. During the course, we will conduct course projects to teach you real-life predictions. One of the projects will teach you how to predict user actions on a website when you are provided with the user’s data. You will learn to predict:

• If the user had a mobile device
• The number of products the user saw
• How long did they stay on the site
• What time of the day did the user visit
• Are they a returning visitor or not

The course will include another project as you reach the end. It will show you how to use your learning for facial expression recognition. If you think about it, after you have finished, you will be able to predict a person’s emotion based on just a picture! Isn’t it fascinating?

Once you are done and dusted with the basics, we will provide you with a brief overview of some new developments in neural networks. This will include somewhat modified structures and their applications.

Here, we would like to make a note that if you are already familiar with softmax and backpropagation and want to skip the theory to speed up your learning process, you can check out the follow-up courses where you can learn advanced techniques with GPU optimisation.

Additional Courses for Advanced Levels

We have some other courses to cover some advanced level topics, such as:

• Convolutional Neural Networks
• Restricted Boltzmann Machines
• Autoencoders

If you have developed a strong understanding of the previous course, you can move forward with advanced topics!

The Main Focus of this Course

The central focus of this course is not just about how you can use a program, but in fact, how to build and understand what’s going on. With this course, we promise that anyone can build an Application Programming Interface in a matter of minutes after reading some basics.

It’s not just about remembering facts but about carrying it out yourself through experimentation. You will learn how to visualise everything that’s happening internally in the model. This course is the right choice for you if your quest for knowledge is beyond a superficial look at machine learning models.

Prerequisites for the Course

Before you begin, you must have an understanding of:

• Calculus
• Linear algebra
• Probability
• Numpy coding: loading a CSV file, matrix/vector operations
• Python coding: loops, if/else, sets, lists

How to Get Through the Course?

These tips will help you throughout the course:

• Take handwritten notes during the lecture to retain the information efficiently
• Ask plenty of questions on the discussion board
• Don’t get impatient if it takes you days or even weeks to finish the exercises

Useful Course Ordering

• Linear Regression in Python
• Logistic Regression in Python
• Supervised Machine Learning in Python
• Deep Learning in Python
• Practical Deep Learning in Theano and TensorFlow
• Convolutional Neural Networks in Python
• Easy NLP
• Cluster Analysis and Unsupervised Machine Learning
• Unsupervised Deep Learning
• Hidden Markov Models
• Recurrent Neural Networks in Python
• Natural Language Processing with Deep Learning in Python

Frequently Asked Questions (FAQs)

How do I access the course?

All you need is a modern browser such as Chrome, Firefox, or Internet Explorer and you will be able to access the course from any computer, tablet, or mobile device.

Is there a specific time duration to complete this course?

Learning with Eskills Academy is super easy. No limitation on-time duration so you learn easily at your own pace and convenience.

Do I get a certificate?

Yes, when you complete the course you will receive a certificate of completion, which you can happily add to your resume or LinkedIn profile.

In what cases will I be eligible for a refund?

All Eskills Academy courses come with Teachable backed 30-day money-back guarantee. If you are not satisfied with the purchased course, refunds are applicable as per our terms mentioned on the website.

Other FAQ’s

What prior knowledge do I need to attend the teacher-led class?

At least six months of professional PC configuration and troubleshooting experience.

How can I access my course materials if I choose this method?

Upon receipt of payment, Eskills will send you an email with all the links and information you need to get started.

What content on-demand will I get?

You will have access to official CompTIA On-Demand content that is constantly updated so you can prepare for your A + exam and stay informed of any content changes during your subscription period.

What laboratories do I have access to?

Gains access to [enter number] preconfigured A + curriculum labs.

How many practice tests are included?

 4A + accredited practice exams are included.

How do I plan my teacher-led training?

As soon as payment has been received, you will receive the details of your training package with unlimited access by email. At this point, you can call or email our customer service team to help you register for the event date you have chosen.

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