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Discover exciting and fast-moving AI fields with online courses. Learn artificial intelligence by studying natural language processing, strengthening training, predictive analysis, deep neural networks, image processing, the human brain, and more.
Online Eduranz Artificial Intelligence Certification Course with TensorFlow is an industry-leading CNN certification training (Conversion Neural Network) for CNN Perceptron, TensorFlow, TensorFlow code, graphic visualization, transfer training and repetitive Deep Learning networks, Hard & TFLearn API, in-depth GPU training, Redistribution and hyperparameter through practical projects. Learn AI with this online artificial intelligence course using TensorFlow
|Section 1: Introduction|
|Introduction to Software Training||FREE||00:40:00|
|Object Oriented Design Patterns||00:35:00|
|Section 2: Advanced Computing|
|Introduction to Azure Data factory||FREE||00:25:00|
|Multi Threading in Softwares||00:40:00|
|Managing Software Testing||00:20:00|
|The Software Quiz||00:04:00|
|Introduction to Speech||FREE||00:40:00|
Artificial Intelligence and Deep Learning is taking over every other industry. From small companies to big tech-giants, all are implementing AI and ML to grow in their respective fields. On the other hand, where AI and ML are so in demand that there is a shortage of skilled Artificial Intelligence Engineer and Machine Learning Engineer. This Artificial Intelligence course by Eduranz is designed and structured by industry experts based on industry requirements and demands. This Artificial Intelligence training program will help you master Python and its libraries for Artificial Intelligence, TensorFlow, and Keras. As part of this project and assignment-based Artificial Intelligence certification program, you will learn about Time Series Analysis, Predictive Analytics, Graphical Models, Reinforcement Learning, Convolutional Neural Networks, Recurrent Neural Networks, and many more
In this Artificial Intelligence Training, you will learn about
• Python, libraries, and its application
• Understanding predictive analytics
• Getting started with machine learning
• Understanding time series analysis
• Graphical models
• Introduction to reinforcement learning
• Learning Natural Language processing
• Introduction to deep learning
• Understanding neural networks with TensorFlow
• Advanced neural networks with TensorFlow deep networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Restricted Boltzmann Machine (RBM) and autoencoders
• Understanding Keras API
• TFLearn API
• Freshers or Graduates
• Anyone willing to have a career in AI & DL
• If you are working in a different programming language (regardless of your experience)
• Data Analysts or Business Analysts
• Any working professionals wanting to shift career into AI and Deep Learning
• BI Managers or Analytics Professionals
• Application or Web Developers
• Software Engineers or ETL Developers
• Big Data Analysts
No prerequisites are required for this Artificial Intelligence Trainings such, but the basics of any programming language will come in handy for you.
Advances in artificial intelligence and data analytics can be seen impacting development in numerous ways in industries across the world. China has committed 150 Billion Dollars aiming to turn into a world chief by the year 2030. On the other hand, the US government is putting $1.1 billion in non-classified AI research. Significantly, this year, Indian government has decided to invest Rs 3,063 crore or $477 Million in the field of AI, ML, and 3-D printing. Not only that, Gartner predicts, by 2020, but AI will also create around 2.3 Million jobs all over the world. Clearly this is the right time to get certified in the field of artificial intelligence.
Average salary of an AI Engineer in India is around Rs. 6,00,000 (Entry-level), average salary of a mid-level and senior-level artificial intelligence engineer could go up to Rs. 50,00,000 and more in India. On the other hand, in the US, it ranges from $38,000 to $231,000.
Eduranz follows a rigorous certification process. To become a certified Data Scientist, you must meet the following criteria:
Online Instructor-led Course
- • Successful completion of all projects, which will be evaluated by trainers
- • Scoring minimum 60% in the Artificial Intelligence and Deep Learning Course Certification Training quiz conducted by Eduranz
- • Completing all course videos in our LMS
- • Scoring minimum 60% in the Artificial Intelligence Certification Training quiz conducted by Eduranz
AI Engineers are responsible to build, test, and deploy artificial intelligence models and AI infrastructure. In addition to that they can also be described as problem solvers who have the expertise to navigate between traditional SDLC and artificial intelligence implementations
- • Amazon
- • Microsoft
- • Walmart
- • IBM
- • Honeywell
- • Facebook (Source: Indeed) and a lot more
In this chapter we will get an overview of all the important concepts of Python, its libraries and applications, to help us kick start the AI training course. Here’s the table of content for this chapter.
- • Basics of Python
- • OOPs Concept in Python
- • Introduction to NumPy
- • Introduction to Pandas
- • Data Pre-processing
- • Data Manipulation
- • Data Visualization
- • Loading different types of dataset in Python
- • Arranging the data
- • Plotting the graphs
- • NumPy
- • Pandas
- • Scikit-learn
- • Matplotlib
Here, we will discuss about predictive analysis and important concepts related to it as shown below.
- • Fundamentals of Statistics
- • Generalized Linear Models
- • Regression and Clustering
This chapter will guide you through the important concepts of Machine Learning and algorithms. Let us take a look at the important concepts added in this chapter.
- • What is Machine Learning?
- • Supervised Learning – Regression
- • Supervised Learning – Classification
- • Model Selection and Boosting
- • Unsupervised Learning
- • Dimensionality Reduction
- • Association Rules Mining and Recommendation
- • Regression Use case: Weather Forecasting
- • Clustering Use Case: Image classification
- • Clustering Use Case: Recommender system
- • Dimensionality Reduction Use Case: Structure Discovery
- • Association Rule Mining
- • Use Case Apriori Algorithm: Market Basket Analysis
This chapter discusses about one of the most important concepts in this course, time series analysis.
- • What is Time Series?
- • Time Series Analysis techniques and applications
- • Components of Time Series
- • Moving average
- • Smoothing techniques
- • Exponential smoothing
- • Univariate time series models
- • Multivariate time series analysis
- • Arima model
- • Time Series in Python
- • Use Case of Checking Stationarity
- • Learn how to convert a non-stationary data to stationary
- • Implement Dickey Fuller Test
- • Use case of ACF and PACF
- • Generate the ARIMA plot
- • Time Series Analysis Forecasting
This chapter will discuss about graphical models. Here’s a list of topics included in this chapter.
- • Understanding graphical model
- • Bayesian Network
- • Inference
- • Model learning
- • Use case Bayesian Network
In this chapter, you will get an introduction to reinforcement learning and how to implement it. Here’s a list of concept you will get to learn in this chapter.
- • Getting started with Reinforcement Learning
- • Bandit Algorithms and Markov Decision Process
- • Dynamic Programming and Temporal Difference Learning methods
- • What is Deep Q Learning?
- • Calculating Reward
- • Discounted Reward
- • Calculating Optimal quantities
- • Implementing Q Learning
- • Setting up an Optimal Action
In this chapter, we will talk about text processing, Natural Language Processing, sentiment analysis, and many more.
- • Text Pre-processing and Nature Language Processing
- • Analyzing Sentence Structure
- • Text Classification
- • Sentiment Analysis
- • Use case: Twitter Sentiment Analysis
- • Use case: Chat Bot
This chapter will highlight fundamentals of deep learning. Here’s an introductory lesson for you to get started with Deep Learning.
- • What is Deep Learning?
- • Why Deep Learning?
- • Advantage of Deep Learning over Machine learning
- • 3 Reasons to go for Deep Learning
- • Real-Life use cases of Deep Learning
This chapter will guide you through workings of Deep Learning and tools that we can use to implement deep learning.
- • How Deep Learning Works?
- • Activation Functions
- • Illustrate Perceptron
- • Train a Perceptron
- • Parameters of Perceptron
- • TensorFlow
- • Graph Visualization
- • Constants, placeholders, and variables
- • Create a Model
- • TensorFlow code- basics
- • Use case Implementation
- • Building a single perceptron for classification on SONAR dataset
In this chapter we will talk about advanced concepts of Neural Networks with TensorFlow.
- • Understand limitations of a Single Perceptron
- • Understand Neural Networks in Detail
- • Illustrate Multi-Layer Perceptron
- • What is a backpropagation?
- • Getting started with TensorBoard
- • Understand Backpropagation with an example
- • Using TensorFlow build MLP Digit Classifier
- • Building a multi-layered perceptron for classification of Hand-written digits
In this chapter we will talk about Deep Networks and all the core concepts related to it. Here’s a list of concepts included in this chapter.
- • What is Deep Network?
- • Why Deep Networks?
- • Understand How Deep Network Works?
- • How Backpropagation Works?
- • Illustrate Forward pass, Backward pass
- • Different variants of Gradient Descent
- • Types of Deep Networks
- • Use-Case Implementation on SONAR dataset
- • Building a multi-layered perceptron for classification on SONAR dataset
This chapter will guide you through the concepts related to Convolution Neural Networks.
- • What is CNN?
- • Application of CNN
- • Architecture of a CNN
- • Convolution and Pooling layers in a CNN
- • Understanding and Visualizing a CNN
- • Learn how to build a convolutional neural network for image classification
In this chapter we will be discussing about recurrent neural networks and how to implement it.
- • Introduction to RNN Model
- • Application use cases of RNN
- • Modelling sequences
- • Training RNNs with Backpropagation
- • Long Short-Term memory (LSTM)
- • Recursive Neural Tensor Network Theory
- • Recurrent Neural Network Model
- • Building a recurrent neural network for SPAM prediction.
This chapter will guide you through all the important concepts related to RBM and autoencoders.
- • Introduction to Restricted Boltzmann Machine
- • Applications of RBM
- • Collaborative Filtering with RBM
- • Getting started with Autoencoders
- • Autoencoders applications
- • Learn how to build an autoencoder model for classification of handwritten images extracted from the MNIST Dataset
Here you will learn how to implement Keras API and how to use Keras with TensorBoard.
- • Getting started with Keras
- • Compose Models in Keras
- What is sequential composition?
- What is functional composition?
- • Predefined Neural Network Layers
- • What is Batch Normalization?
- • Save and Load a model with Keras
- • Customize the model training process
- • Use case Keras implementation
- • Learn how to build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio
- • Using TensorBoard with Keras
This chapter will elaborate implementation of TFLearn API with use cases. Here’s a list of concepts you will be learning in this chapter.
- • What is TFLearn?
- • Compose Models in TFLearn
- Sequential Composition
- Functional Composition
- • Predefined Neural Network Layers
- • Batch Normalization
- • Save and Load a model with TFLearn
- • Customize the Training Process
- • Use case Implementation with TFLearn
- • Use TensorBoard with TFLearn
- • Build a recurrent neural network using TFLearn to do image classification on hand-written • digits
Project 1: Credit Card Fraud Detection
Problem Statement: Make a Hybrid Deep Learning Model by Analyzing the Credit Card Applications Data Set.
Industry: Banking Sector
Description: Build a model to identify the frauds from the Data Set. Then, move to building an advance deep learning mapping model to identify and predict the probability that each customer cheated.
- First, build the unsupervised deep learning branch from your hybrid deep learning model.
- Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.
- Machine Learning
- Deep Learning
- Supervised Deep Learning
- Unsupervised Deep Learning
Project 2: Face Detection
Problem Statement: Build a Machine Learning model which should be able to detect multiple faces when shown. Use OpenCV to perform the operations.
Description: Provide your machine with the multiple dataset of multiple persons in OpenCV, and after running it, it should be able to detect the those faces and display their names whenever detected on the screen or camera.
- A Dataset which should consist of everyone’s face images into subfolder by names
- Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model
- Deep Learning
At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.
24/7 Tech Adviser Support
Lifetime 24/7 Technical and Non Technical Support from team Eduranz.
Get Lifetime opportunity to access and attend the live sessions multiple times.
Assignments & Quizzes
Every module will be followed by certification based assessment and quiz.
Become a Certified Professional.
Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.
Live Virtual Classes or Online Classes: With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time. Independent study online: In this mode, you will receive videos with lectures and can continue the course as you wish. Win Python Portable Distribution is an open-source environment where all the exercises are immediately carried out. Installation instructions are given during training.
This Eduranz Artificial Intelligence course gives you hands-on experience in mastering one of the best programming languages, Python. In this Artificial Intelligence online course, you will learn basic and advanced Python concepts, including MapReduce in Python, machine learning, streaming Hadoop, and Python packages such as Scikit and Scipy. After successfully completing training, you will receive an Eduranz Attendance Certificate. As part of this Artificial Intelligence online course, you work on real-time projects that are very important in the corporate world, on step-by-step assignments and curricula developed by industry experts. After completing the Artificial Intelligence course, you can apply for some of the best jobs in the top-ranked MNC in the world. Eduranz provides lifetime video access, tutorials, 24×7 support, and upgrades to the latest version at no additional cost. Therefore it is definitely a one-time investment for a practical Python online course.
Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing the training. We also don’t limit the number of tickets you can collect when solving questions and doubts.
Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of the learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.
Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months of intensive industry experience.
Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV.
In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.
After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; You received a certificate that was certified by Eduranz. This certification is recognized by Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.
In our job support program, we help you start your dream job by sharing your resume with potential tenants, helping you make resumes, and preparing you for interview questions. Eduranz training should not be seen as an employment agency or employment guarantee, because the entire employment process is handled directly between the student and the employing company and the final choice is always left to the employer.