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Online Deep Learning Certification Course
You get access to all 40 courses, Projects bundle. You do not need to purchase each course separately.
203+ Video Hours
Learn and apply concepts of deep learning with live projects. It includes a conceptual and practical understanding of Neural Networks, functions Tensorflow
Anyone serious about learning Deep Learning Course and wants to make a career in this Field
Basic knowledge about Machine Learning would be preferable
What do you get?
Certificate of Completion for each of the 40 courses, Projects
Course Completion Certificates
Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced data analytics skills
Type of Training
Video Course – Self Paced Learning
1 GB RAM or higher
Speaker / HeadphoneDeep Learning Course Curriculum
This training has been solely focused on Deep Learning which is one of the most important sub-modules of Machine learning. The eventual target of this course is to make the trainees cognizant about the concepts of Deep learning by the virtue of dedicated training and projects.Objectives Course Highlights
The course has been drafted very carefully to meet the expectations of the trainees regardless of their familiarity with Deep Learning or Artificial Intelligence. Here is a glimpse of what we will be learning from the course together with the take away from this training.
Machine Learning with Tensorflow will be covered at the beginning of the course where we will be learning how to implement the procedures of Machine learning by leveraging Tensorflow.
Deep Learning Tutorials & Hands-on Deep Learning Training are the other topics that the tutorial will be focused on. Being focused to deliver the hands-on experience, units have been introduced to satisfy the purpose.
Matplotlib for Python Developers has been introduced in this course which is important from the view of Deep learning. Beginners have been considered while developing this course making it very easy for newcomers to learn this concept.Project Highlights
To improve the proficiency of the trainees, there are several projects included in this course. Projects will be mainly based on the topics that are covered under the course and will also be very specific to the deep learning-based concepts
Project on Tensorflow has been included in the course. The project will be focused on Implementing Linear Model with Python. Through this project, you will be learning how to implement the linear model in the application using the predefined functions or frameworks in python.
Project on Pandas will be the third project in the unit which will be focused on managing the high count of data. The topic will be Data Management for Retail Dataset. You will learn how the data could be leveraged for the Retail dataset to conduct the smooth flow of operations.
As an outcome of completing these projects, the trainees will become amply capable to develop the application using R or Python programming language where features of Deep learning or Artificial intelligence have to be implemented.Deep Learning Course – Certificate of Completion What is Deep Learning?
The idea of deep learning started with the invention of the neural network. The neural network is inspired by the design of our brain and it tries to create a model of our brain. The fundamental idea behind the neural network was to create a system that can mimic our brain i.e. it can process information as our brain does.
Deep learning is a special type of architecture that exploits the concept of neural network and design a system of neurons which has many layers of hidden units (hence the name deep), these neurons are connected and send and receive information from each neural. Using the concept of weight propagation, gradient descent, and activation functions, these neurons learn the pattern from input data and then uses its learning to classify or predict any unknown new data points.
This deep learning course teaches the following topics:
Prediction in Structured/Tabular Data: this technique teaches deep learning methods on tabular data such as RDBMS tables or excels data.
Recommendation: Here students learn about recommendation systems such as those used by Amazon and Netflix.
Image Classification: Image classification is core to deep learning, the MNIST dataset is quite popular for this.
Image Segmentation: Such as finding dogs in the picture of dogs and cats. These are state of the art application of deep learning.
Object Detection: such as locating which images are of dogs and which images are of a cat in a group of thousands of images.
Style Transfer: Transfer learning is a subfield of deep learning.
Sentiment Analysis: From given text documents, finding if the writer is positive or negative in his tone.
Text Generation: Automatically generating text such as YouTube video transcription.
Time Series (Sequence) Prediction: Time series data such as stock movement can be predicted using deep learning.
Machine Translation: translation from English to French can be done using deep learning, for example.
Speech Recognition: between voice samples of Obama and Clinton, a deep learning method can identify which voice sample is of which person.
Question Answering: Automatic answer generation from the question can also be done using deep learning.
Text Similarity: finding which text samples are similar.
Image Captioning: creating a caption of an image based on what is there in the image.
This course teaches a lot of relevant deep learning skills which are quite in demand in the market. Such as below:
Object Detection: This is related to image and video analysis and also called as computer vision.
Speech Recognition: This comes under the natural language processing framework.
Sentiment Analysis: This also comes under NLP
Single Node and Multi-Node Neural Network: This teaches the architecture of the neural network
Neural Network: the Basic building block of a neural network
Keras and CUDA: A framework for massively parallel processing for deep learning based on GPU.
Pytorch Framework: A python framework to be used for deep learning. Very powerful.
TensorFlow Framework: A deep learning framework developed by Google. It is getting very popular these days.
CNN: Also called a convolutional neural network, mostly used for image data
RNN: Recurrent neural network, used to memorizing sequences of patterns such as text data.
Language Translation: Deep learning-based language translation
Emotion Detection: Detecting the emotion from audio or video message similar in idea with sentiment analysis but approach differs.
The specific list of pre-requisites is as below:
Basic knowledge of machine learning required such as supervised and unsupervised learning, linear and logistic regression, etc.
High school level knowledge of mathematics and statistics is also needed. You may want to revise some of these if you seem to have forgotten what you learned in high school or junior college. Some topics such as probability and linear algebra are particularly important and indispensable.
Basic knowledge of programming and hands-on experience with at least programming language is required. Particularly, if you have been using python before, this course becomes a little easy otherwise you may want to follow a python tutorial and get some basic idea of it before starting with this course.
The Deep learning training course is intended for machine learning engineers or
who are already having a few years of working experience in this field? As mentioned in the previous section, to learn and understand deep learning, one should know machine learning beforehand.
In this section, we explain what type of people are suitable for this deep learning certification. The list is as below: –
Junior Data Scientists: People who already know machine learning but now want to learn deep learning.
Data Engineers: These are those people who work with databases such as database developers, database administrators, etc.
Analysts: People such as business intelligence guys, data analysts, data visualization guys, etc.
Architects: Senior and junior architects who specialize in product development and solution management etc.
Software Engineers: Such as Java or C developers, Android or iOS developers, etc.
IT Operations: Such as network administrator, network security guys, etc.
Technical Managers: People who want to lead and manage an expert on machine learning professionals in their team.Deep Learning Training Course – FAQ’s
In this section, we provide some common questions which candidates often ask before enrolling for the course.How much mathematics do I need to know to understand this deep learning training certification?
The requirements for this course are explained in the pre-requisite section. You need to know basic probability such as probability distribution, conditional probability, statistics, and some linear algebra to fully understand deep learning.Will this Deep Learning course help me with participating in Kaggle competition?
Yes. After completing this course, you can start participating in deep learning competitions on Kaggle or another website.How much is the course fee?
For details on the course fee, you can see the fee section of this page or contact our team. Various teams, we run some offers and discounts, etc, and the details regarding the same can be obtained via our customer support team.Is there a scholarship provided for this Deep learning training?
We provide scholarships or course discount based on various factors, please get in touch with the support team for details.
In this section, we illustrate the various benefits of this course. After completing the course you can work at various capabilities in a varied role within an organization. Specific roles are mentioned below.
Data Analyst: This is the position for beginners.
Data Scientist: This is the position for 2-5 years of experienced professionals.
Software Development Engineer: This position is for software developers who are also a data scientist from time to time.
Software Developer: Full-time developers, they design API and Services of deep learning for example.
Research Scientist: They focus on research activities in deep learning.
Data Analyst: Junior level persons who are focusing more on the data side and not on the algorithm side.
Business Analyst: Traditional BI folks who may now move towards ML and AI.
Hadoop Developer: Big data developers for large scale data applications.
Researcher: Theoretical researches in deep learning.
R Programmer: R is a programing language for ML and data science.
Machine Learning Engineer: Similar to data scientists.
Machine Learning Developer: Similar to Data Scientist. Different companies often give different designations.
Chatbots Developer: These guys specifically focus on NLP and chatbots.
Python Developer: They build machine learning and other applications on Python. Python is the most popular language for machine learning and deep learning.
Python ML Engineer Data Scientists: Who are specialized in Python. This course introduces two ML framework PyTorch and TensorFlow and both are based on Python.Deep Learning Training Course Reviews Deep Learning
LinkedGreg Kowalczyk Very Practical
I enrolled in this deep learning certification after taking the machine learning course from this academy. I must say both the courses are fabulous. Deep learning course specifically focuses on teaching complicated concepts into an easy to understand manner. I am from a non-technical background and thus initially I was skeptical about the content but now I am 100% satisfied. This deep learning training is easy to understand for a non-tech person as well. After the course, I started participating in online deep learning completions and challenges which was difficult for me earlier. I recommend this course to everyone who wants to learn deep learning.John Johnson Nice learning Radhika Rohan Apte
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