DEEP LEARNING
Specialization | 40 Course Series | 4 Mock Tests
This Online Deep Learning Certification includes 40 courses with 153+ hours of video tutorials and One year access and several mock tests for practice. 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
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What you'll get
- 153+ Hours
- 40 Courses
- Mock Tests
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Courses: You get access to all 40 courses, Projects bundle. You do not need to purchase each course separately.
- Hours: 153+ 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: One year 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 40 courses, 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
Content
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MODULE 1: Deep Learning & Tensorflow Essentials Training
Courses No. of Hours Certificates Details Machine Learning ZERO to HERO - Hands-on with Tensorflow 13h 03m ✔ Deep Learning: Neural Networks with R 2h 56m ✔ Deep Learning: Heuristics using R 4h 42m ✔ Deep Learning ZERO to HERO - Hands-on with Python 11h 17m ✔ Deep Learning Tutorials 1h 34m ✔ Project on Tensorflow - Implementing Linear Model with Python 1h 46m ✔ Deep Learning: Artificial Neural Network ANN using Python 2h 29m ✔ Deep Learning: Convolutional Neural Network CNN using Python 1h 06m ✔ Deep Learning: Project using Convolutional Neural Network CNN in Python 1h 02m ✔ Deep Learning: RNN, LSTM, Stock Price Prognostics using Python 2h 17m ✔ -
MODULE 2: Machine Learning & Ai
Courses No. of Hours Certificates Details Machine Learning with R 20h 25m ✔ Artificial Intelligence and Machine Learning Training Course 12h 13m ✔ AI Artificial Intelligence & Predictive Analysis with Python 6h 15m ✔ AI Machine Learning in Python 8h 37m ✔ Predictive Analytics and Modeling with Python 8h 26m ✔ Matplotlib for Python Data Visualization - Beginners 4h 12m ✔ NumPy and Pandas Python 4h 8m ✔ -
MODULE 3: Learning from Practicals & Case Studies
Courses No. of Hours Certificates Details Pandas Python Case Study - Data Management for Retail Dataset 3h 22m ✔ Python Case Study - Sentiment Analysis 57m ✔ Data Science with Python 4h 18m ✔ OpenCV for Beginners 2h 28m ✔ Seaborn Python - Beginners 2h 28m ✔ PySpark Python - Beginners 2h 16m ✔ Machine Learning using Python 3h 26m ✔ Statistics Essentials with Python 3h 23m ✔ -
MODULE 4: Advanced Projects based Learning
Courses No. of Hours Certificates Details Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression 2h 07m ✔ Logistic Regression using SAS Stat 4h 26m ✔ Linear Regression & Supervised Learning in Python 2h 28m ✔ Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔ Predictive Analytics Model for Term Deposit Investment with R Studio 3h 2m ✔ Project on R - Card Purchase Prediction 2h 28m ✔ Develop a Movie Recommendation Engine 51m ✔ Random Forest Techniques and R - Employee Attrition Prediction 1h 6m ✔ Predictive Analytics Model for Term Deposit Investment using CART Algorithm 1h 38m ✔ Predicting Credit Default using Logistic Regression in Python 3h 3m ✔ House Price Prediction using Linear Regression in Python 3h 2m ✔ Poisson Regression with SAS Stat 2h 21m ✔ Machine Learning Project using Caret in R 1h 58m ✔ Cluster Analysis and Unsupervised Machine Learning - K-Means Clustering using R 43m ✔ -
MODULE 5: Mock Exams & Quizzes
Courses No. of Hours Certificates Details Test - Deep Learning Test Series Test - Mock Exam Deep Learning Test - Deep Learning Practice Exam Test - Complete Deep Learning Exam
Description
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.
Sample Certificate
Requirements
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- 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.
Target Audience
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- The Deep learning training course is intended for machine learning engineers or
data scientists
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- 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.
Course Ratings
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I found this course very helpful. The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills
JIYEON CHOI
Very Informative and Well Organized Course Contents. High Quality Videos. I will recommend this course to anyone I know who interested to learn about Data Analytics.
JOSEPH WONG
I recently completed a data analytics course and found it to be an incredibly valuable learning experience. The course provided a comprehensive introduction to data analytics, covering everything from data collection and cleaning to advanced statistical analysis and data visualization. One thing I appreciated about the course was the hands-on approach to learning. Throughout the course, we worked with real datasets and used industry-standard tools such as Python, R, and Tableau to analyze and visualize the data. This gave me the practical skills and experience I needed to feel confident in my ability to work with data in a professional setting. The course instructors were knowledgeable and engaging, and they were always available to answer questions and provide feedback. The course also had a supportive and active online community, where I was able to connect with other learners and share my experiences and insights. Overall, I would highly recommend this data analytics course to an
Akram Ahmed
The Data Science Fundamentals online course that I recently completed. Overall, I found the course to be highly valuable and informative. The content was well-structured and provided a solid foundation for understanding key concepts in data science.
Priti Gajanan Patole