A I A N A L Y T I C S

About Course

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. Artificial Intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning, Problem-solving.
Artificial intelligence (AI, also Machine Intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.

01
Introduction to Deep Learning
  1. Deep Learning: A revolution in Artificial Intelligence
  2. Limitations of Machine Learning
  3. Discuss the idea behind Deep Learning
  4. Advantage of Deep Learning over Machine learning
  5. 3 Reasons to go Deep
  6. Real-Life use cases of Deep Learning
  7. Scenarios where Deep Learning is applicable
  8. The Math behind Machine Learning
  9. Review of Machine Learning Algorithms
02
Fundamentals of Neural Networks with Tensorflow
  1. How Deep Learning Works?
  2. Activation Functions
  3. Illustrate Perceptron
  4. Training a Perceptron
  5. Important Parameters of Perceptron
  6. What is Tensorflow?
  7. Tensorflow code-basics
  8. Graph Visualization
  9. Constants, Placeholders, Variables
  10. Creating a Model
  11. Step by Step - Use-Case Implementation
03
Deep Dive Into Neural Networks With Tensorflow
  1. Understand limitations of A Single Perceptron
  2. Understand Neural Networks in Detail
  3. Illustrate Multi-Layer Perceptron
  4. Backpropagation – Learning Algorithm
  5. Understand Backpropagation – Using Neural Network Example
  6. MLP Digit-Classifier using TensorFlow
  7. TensorBoard
04
Master Deep Networks
  1. What is TensorFlow?
  2. Use of TensorFlow in Deep Learning
  3. Working of TensorFlow
  4. How to install Tensorflow
  5. HelloWorld with TensorFlow
  6. Running a Machine learning algorithms on TensorFlow
05
Convolutional Neural Networks (CNN)
  1. Introduction to CNNs
  2. CNNs Application
  3. Architecture of a CNN
  4. Convolution and Pooling layers in a CNN
  5. Understanding and Visualizing a CNN
  6. Transfer Learning and Fine-tuning Convolutional Neural Networks
06
Recurrent Neural Networks (RNN)
  1. Intro to RNN Model
  2. Application use cases of RNN
  3. Modelling sequences
  4. Training RNNs with Backpropagation
  5. Long Short-Term memory (LSTM)
  6. Recursive Neural Tensor Network Theory
  7. Recurrent Neural Network Model
07
Restricted Boltzmann Machine(RBM) And Autoencoders
  1. Restricted Boltzmann Machine
  2. Applications of RBM
  3. Collaborative Filtering with RBM
  4. Introduction to Autoencoders
  5. Autoencoders applications
  6. Understanding Autoencoders
08
Keras
  1. Define Keras
  2. How to compose Models in Keras
  3. Sequential Composition
  4. Functional Composition
  5. Predefined Neural Network Layers
  6. What is Batch Normalization
  7. Saving and Loading a model with Keras
  8. Customizing the Training Process
  9. Using TensorBoard with Keras
  10. Use-Case Implementation with Keras
09
Tflearn
  1. Define TFlearn
  2. Composing Models in TFlearn
  3. Sequential Composition
  4. Functional Composition
  5. Predefined Neural Network Layers
  6. What is Batch Normalization
  7. Saving and Loading a model with TFlearn
  8. Customizing the Training Process
  9. Using TensorBoard with TFlearn
  10. Use-Case Implementation with TFlearn

Pre-Requisites

Graduate /PG with computer science Or Mathematical science. To be able work with various programming technologies such Java, Python, Scala and Database concepts is very important. Developer should learn these programming languages to develop software system that can take data from data store and use it for machine learning. Skills of formatting data formats, process the data to make it compatible with the machine learning algorithm is also a prerequisite for machine learning.

Basic Database skills: You should have prior knowledge to work with relational and NoSQL databases. In your machine learning program you will have to use data sets from many different data source at a time. Programmers usually read the data from different data source and then convert it in a format that can be used by machine learning framework.

Basic Mathematics skills: Linear algebra, Probability theory,Calculus,Calculus of variations,Graph theory,Statistics and Probability, Differential equations, Mathematical statistics, Optimization, Regression and Time Series, Probability Distributions, Hypothesis Testing, Bayesian Modeling, Fitting of a distribution

Mini Project

Business Objective:   John is the Customer Services and Relations head for a Multi brand retail store. He analyzed a couple of reports and got worried about losing his customers overtime. He thought over the different customer segments presented in the report and concluded that not all customers were worth retention. He identified the loyal and profitable customer segment and planned to develop a churn model to gauge the propensity of attrition of this customer segment. He had plans to revise promotions and schemes for these customers based on the significant factors contributing to their attrition. John will use Churn model primarily to identify the customers next in line to attrite. He will then plan the promotions and strategies to retain them.

Business Problem:   Mr. A is a strategic marketing head of a Multi product Insurance Company. He has a large database of general insurance (GI) customers. He wants to cross sell health insurance products to his general insurance customers. The limited budget and low responses of the healthcare campaigns force him to think of an approach for targeted marketing. He needs to identify and capture the characteristics of customers who buy health insurance. The implementation of the same will involve design and development of a predictive model, selection of the population as model input and deployment and refresh process for regular use.

Job Opportunities

  • Data Scientist
  • Data Engineer
  • Data Architect
  • Data Administrator
  • Data Analyst
  • Business Analyst
  • Data/Analytics Manager
  • Business Intelligence Manager
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Data Engineer
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Data Analyst
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Data Scientist

Satisfied Clients

Sanjay Patil

Personally, Python was not my strongest subject, and I needed some coaching to get this done right. Enrolled into the A.I. Analytics Python class, this was the best thing that could happen to me professionally. Wonderful and excellent training by this IT institute! Totally recommend the A.I. Analytics if you are looking for python training institute in Pune.

Amol

My Friend Suggested me A.I. ANALYTICS. That he recently trained in A.I. Analytics and placed in a MNC. All the trainers are very professional. The way they handle the classes are extra ordinary. Fees is also affordable. This is the best institute for data science

Ritesh Tiwari

Everything is really good at A.I.Analytics. The trainer is really awesome. Having so much patience to answer our questions, he has a wonderful knowledge about what he needs to teach. He also explains the topics which are outside the context to better understand the course. He is very keen towards answering, giving great examples, debating the technical aspects .

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