Date Days Time
February 7 Sat & Sun
Weekend batch
Filling Fast
8:00 PM to 11:00 PM (IST)
February 14 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)
February 21 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)
February 28 Sat & Sun
Weekend batch

8:00 PM to 11:00 PM (IST)

Program Price

$129/- $258/-

50% off limited time offer

Program Syllabus

  • • 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
  • • Fundamentals of Statistics
  • • Generalized Linear Models
  • • Regression and Clustering

  • • 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
  • • What is a 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
  • • Understanding graphical model
  • • Bayesian Network
  • • Inference
  • • Model learning

  • • Use case on Bayesian Network
  • • Module 6: Introduction to Reinforcement Learning
  • • 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
  • • Text Preprocessing and Natural Language Processing
  • • Analyzing Sentence Structure
  • • Text Classification
  • • Sentiment Analysis

  • • Use case: Twitter Sentiment Analysis
  • • Use case: Chat Bot
  • • 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

  • • 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
  • • 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
  • • 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
  • • 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
  • • 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.
  • • 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
  • • 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
  • Provide your machine with the multiple dataset of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into subfolders by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model

  • Build a model to identify the frauds from the Data Set. Then, move to building an advanced 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.


Artificial Intelligence and Deep Learning Certification Training

Become an Artificial Intelligence and Deep Learning Engineer by mastering concepts from basics to advanced with this course, designed by experts, filled with real-world projects and hands-on exercises

Key Features

  • 5 + Projects, hands-on, and case studies

  • 36+ Hours of interactive learning

  • 30+ Hours of exercise and project work

  • Lifetime access to LMS

  • Attend as many batches for lifetime

  • 24/7 Technical Support

  • Resume Building

  • Placement Assistance

  • Dedicated Learner Delight Team

About Course

Python programming, TensorFlow, Keras, Tflearn API, TensorBoard, Supervised Learning, Unsupervised Learning, Time Series Analysis, Graphical Model, Bayesian Network, Natural Language Processing, Neural Network, Deep Network, CNN, RNN, RBI, and more

Freshers, anyone willing to build a career as an Artificial Intelligence Engineer

No prerequisite. We teach everything from scratch.

Gartner predicts, by 2020, AI will create around 2.3 Million jobs all over the world. The Indian government has decided to invest Rs 3,063 crore or $477 Million in the field of AI, ML, and 3-D printing. The salaries of AI Engineers in India and in the US can go up to, 50 lacs/yr. and $231k/yr. respectively.

Hiring Companies

Source : Indeed

Our Learners Work For

Course Completion Certificate


Divesh Loomba

Divesh Loomba

Board Advisor, Enterprise Solutions, IOT Evangelis

I have completed my Training on Artificial Intelligence and Deep Learning. The trainer was approachable, well experienced, always interested to explain things. It was a nice experience learning the complex Concepts in Deep Learning.

Lalit Chauhan

Lalit Chauhan

Senior Reseach Analyst

The instructor was very good and prompt in responding to questions. Excellent virtual class experience. Good Work eduranz!

Naresh Pai

Naresh Pai


I was enrolled into Deep Learning training from Eduranz On a professionalism, they provide a great presentation on the topic that helps to understand the in depth of Neural Concepts.

Rajeev Kathuria

Rajeev Kathuria

West Head-Partner Management at Samsung

The instructor was very good and prompt in responding to questions. Excellent virtual class experience. Good Work eduranz!

Ljiljana Spasovic Botha

Ljiljana Spasovic Botha

Business Development Officer at SASLO

Had a great learning session where the concepts are clear to understand and can solve the given assignments easily.

Train Your Employees

We offer flexible and cost-effective group membership for your business, goverment organization.

Connect Now


Eduranz offers a unique online Python Certification course for professionals who are willing to build a career in this rousing domain. There are many reasons to choose Eduranz: Interactive online instructor-led live classes conducted by SMEs
Personal mentors who will keep a stage track of your progress in the course
A Substantial LMS which allows the users to view their recorded sessions from their live classes along with the self-recorded courses
Real-time exercises, assignments, industry-based use cases and real-world projects
24/7 learning support by the Eduranz’s dedicated tech support team
Large community of learners from across the globe
Industrially as well as globally recognized certificate by Eduranz
Personalized job support, resume and interview preparation

You never miss any lecture at Eduranz, because you will be provided with the recorded sessions of the live class on your LMS within 24 hours and despite that, you can also attend any different live session to cover up the missed topic and ask your doubts from the trainer or you can simply reschedule your batch and get yourself a new batch assigned.

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.

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.

Yes, Eduranz has a dedicated placement assistance team. Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with.

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.

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 will receive an industrial recognized certificate by Eduranz. This certification is recognized by companies all across the industry, which includes a lot of top MNCs worldwide.

Our job assistance program will help you reach the job you have been seeking. Under this program, we help you by building your professional resume and then sharing it across our network companies that we have tie ups with. You will also be prepared for interviews through mock sessions. However, Eduranz is not a recruitment agency. We do not guarantee you a job. After we share your profiles with the companies, the further process depends upon your performance and their decision.

All of our highly qualified instructors are industry experts with minimum 10-12 yrs. of relevant IT experience. Each of them underwent a rigorous selection process that included screening profiles, teaching assessments, and training demonstrations.

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