Deep Learning Training (15 Courses, 4+ Projects)
15 Online Courses
4 Hands-on Projects
Verifiable Certificate of Completion
Machine Learning with Tensorflow
Hands-on Deep Learning Training
Deep Learning Tutorials
Project on Tensorflow - Implementing Linear Model with Python
Machine Learning with R
What you get
Mobile App Access
Online Deep Learning Course
This Online Deep Learning Certification Course includes 15 comprehensive courses with 100+ hours of video tutorials and Lifetime Access. You get to learn and apply concepts of deep learning with live projects. This Deep Learning Training includes a conceptual and practical understanding of Neural Networks, functions Tensorflow.
Deep learning is a specific branch of machine learning and artificial intelligence. It is known for its performance on solving complicated problems such as self-driving cars, image classification, video classification, machine translation, speech to text conversion, etc.
In this post, we shall understand what deep learning is capable of doing and introduce a deep learning course for our potential students. The course described here is quite useful for those people who are interested in learning deep learning and wants to work in this area.
Deep Learning is a rather new subfield of Machine Learning domain of computer science. It has been introduced mostly by Mr. Jeffery Hinton and it is attributed for the objective of moving the field of Machine Learning closer to its parent field of Artificial Intelligence. Machine learning can be understood as a scientific discipline which focuses on exploring the construction and development of algorithms that specializes in learning patterns from data. Such algorithms often operate by building a model from inputs data and using the same to make predictions classifications or decisions. Machine learning is also very closely related to statistics and overlaps with computer science, programming, and hacking.
We shall delve deeper into the details of this field and course offerings in the below section.
Industry Growth Trend
[Source - MarketsandMarkets]
[Source - Indeed]
About Deep Learning Certification Course
|Course Name||Online Deep Learning Certification Course|
|Deal||You get access to all 15 courses bundle. You do not need to purchase each course separately.|
|Hours||100+ Video Hours|
|Core Coverage||Learn and apply concepts of deep learning with live projects. It includes a conceptual and practical understanding of Neural Networks, functions Tensorflow|
|Course Validity||Lifetime Access|
|Eligibility||Anyone who is serious about learning Deep Learning Course and wants to make a career in this Field|
|Pre-Requisites||Basic knowledge about Machine Learning would be preferable|
|What do you get?||Certificate of Completion for each of the 15 courses|
|Certification Type||Course Completion Certificates|
|Verifiable 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|
|System Requirement||1 GB RAM or higher|
|Other Requirement||Speaker / Headphone|
Deep Learning Course Curriculum
In this section, each module of the Deep learning Certification Course is explained.
Hands-on Deep Learning Training
This Deep learning training module is more than 3 hours long and contains 24 very informative and conceptual videos. It teaches the following ideas of deep learning: Introduction to hands-on deep learning What is machine learning, Popular ml methods, What is deep learning, Applications of deep learning, Recommendations, Basic concepts of deep learning, Perceptron, Neural network, Universal approximation theorem, Deep neural network, Jupyter notebook, Google colab, Pytorch, Tensors, Gradients, MNIST examples, Check sample, Hidden layer, Interface to a digit
Deep Learning Tutorials
This deep learning training is about 2 hours long and contains 14 videos for learning purpose. It teaches the following concepts to the user: Introduction to deep learning, Structure of neural network, Moving through the neural network, Types of the activation function, Optimizing backpropagation, Briefing on TensorFlow, Installation of TensorFlow, Implementation of neural package, Data for classifier, Implementation with Keras, Values in dataset, Components in dataset, Models in dataset.
Project on Tensorflow - Implementing Linear Model with Python
This deep learning certification course discusses the following learning material in it: Introduction to TensorFlow with Python, Installation of TensorFlow, Basic data types of TensorFlow, Implementing a simple linear model, Creating a python file, Optimization of variables, Implementing the constructor variable, Printing the variable result, Naming the variables, Addition and random number creation.
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 which can mimic our brain i.e. it can process information as our brain does.
Deep learning is a special type of architecture which 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 to each other 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, MNIST dataset is quite popular for this.
- Image Segmentation: Such as finding dogs in the picture of dogs and cats. These are state of 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, 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 in nature.
- Image Captioning: creating a caption of an image based on what is there in the image.
What tangible skill will you learn from this course?
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 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: 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 pattern 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.
- This is an advanced course in the area of machine learning and artificial intelligence, hence user needs to know a few fundamental aspects of machine learning and other related topics before enrolling for this course.
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 wants to learn deep learning.
- Data Engineers: These are those people who work with databases such as database developers, database administrator, 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 is 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 on deep learning competitions on Kaggle or another website.
How much is the course fee?
For details on course fee, you can see the fee section of this page or contact our team. Various team, 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 capability 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 designation.
- 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
Radhika Rohan Apte