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Learn from Home Offer
Deep Learning Training (18 Courses, 24+ Projects)
This Online Deep Learning Certification includes 18 courses, 24 Projects with 145+ hours of video tutorials and Lifetime access.
You get to learn and apply concepts of deep learning with live projects. Thistraining includes a conceptual and practical understanding of Neural Networks, functions Tensorflow
Machine Learning with Tensorflow
Hands-on Deep Learning Training
Deep Learning Tutorials
Project on Tensorflow - Implementing Linear Model with Python
Machine Learning with R
* One Time Payment & Get Lifetime Access
What you get in this Deep Learning Training?
Mobile App Access
About Deep Learning Certification Course
|Course||No. of Hours|
|Machine Learning with Tensorflow||12h 44m|
|Deep Learning Neural Network with R||2h 56m|
|Deep Learning Heuristic using R||4h 7m|
|Hands-on Deep Learning Training||10h 8m|
|Deep Learning Tutorials||1h 34m|
|Project on Tensorflow - Implementing Linear Model with Python||1h 46m|
|Project on Deep Learning - Artificial Neural Network||2h 31m|
|Project on Deep Learning - Convolutional Neural Network||1h 06m|
|Project on Deep Learning - Handwritten Digits Recognition using Neural Network||1h 02m|
|Project on Deep Learning - Stock Price Prognostics||2h 18m|
|Machine Learning with R||20h 28m|
|Artificial Intelligence and Machine Learning Training Course||12h 15m|
|AI Artificial Intelligence with Python||6h 15m|
|Machine Learning with SciKit-Learn in Python||8h 37m|
|Machine Learning Python Case Study - Predictive Modeling||8h 27m|
|Matplotlib for Python Developers - Beginners||4h 12m|
|NumPy and Pandas Python||5h 01m|
|Pandas Python Case Study - Data Management for Retail Dataset||3h 28m|
|Python Case Study - Sentiment Analysis||1h 06m|
|Data Science with Python||4h 14m|
|OpenCV for Beginners||2h 28m|
|Seaborn Python - Beginners||2h 28m|
|PySpark Python - Beginners||2h 3m|
|Machine Learning using Python||3h 26m|
|Statistics Essentials with Python||3h 23m|
|Machine Learning Python Case Study - Diabetes Prediction||1h 03m|
|Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression||2h 07m|
|Logistic Regression using SAS Stat||4h 33m|
|Linear Regression in Python||2h 28m|
|Python Data Science Case Study - Predicting Survival of Titanic Passengers||2h 6m|
|R Practical - Predictive Model for Term Deposit Investment||3h 2m|
|Project on R - Card Purchase Prediction||2h 28m|
|Machine Learning Python Case Study - Develop Movie Recommendation Engine||51m|
|R Practical - Employee Attrition Prediction using Random Forest Technique and R||1h 6m|
|Project on Term Deposit Prediction using Logistic Regression CART Algorithm||1h 38m|
|Project - Credit Default using Logistic Regression||3h 2m|
|Project - House Price Prediction using Linear Regression||3h 12m|
|Poisson Regression with SAS Stat||2h 22m|
|Machine Learning Project using Caret in R||1h 58m|
|Machine Learning Project - K-Means Clustering using R||43m|
|Course Name||Online Deep Learning Certification Course|
|Deal||You get access to all 18 courses, 24 Projects bundle. You do not need to purchase each course separately.|
|Hours||145+ 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 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 18 courses, 24 Projects|
|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.
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.
The objective of this tutorial is to cover all the essential topics that fall under the court of machine learning. Throughout the course, we will be walking through the beginner, intermediate and advanced level concepts. Machine Learning, Tensorflow, R programming language, Artificial Intelligence, SciKit-Learn, etc will be the center of focus in this course and all the modules covered here will be revolving around these topics. You will be mastering each of these concepts that you can apply to solve the organizational problems which need expertise in Deep Learning for resolution.
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.
- R programming language will be practiced in this course which plays a vital role in implementing the concepts of Machine learning. In addition to R, the course will also be detailing advanced functions of Python which will be used while implementing ML at the application level.
- Artificial Intelligence and Machine Learning Training Course, Artificial Intelligence with Python, etc are python based units. Through this, you will be learning how to implement the concepts of AL and AI by the virtue of python programming language. It is interesting to note we will also be learning the advanced concepts of python in this course which is required to work with ML and AI.
- 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.
- NumPy and Pandas are the final essential topics that will be covered in this course. The main reason for the introduction of these concepts is to help trainees learn the advanced aspects of machine learning and artificial intelligence.
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.
- The next projected will be on Machine learning. The topic of the project will be Predictive Modeling with Python. By this project, we will be doing hands-on to create a model that will be containing the juice or prediction. All the advanced level understanding of Deep learning and python will be used to work on this project. After finishing this project, you will become able to develop applications where predictive modeling has to be introduced.
- 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.
Industry Growth TrendThe machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
[Source - MarketsandMarkets]
[Source - Indeed]
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 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.
- 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 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
Radhika Rohan Apte