Artificial Intelligence Engineer Master Course

About Intellipaat Intellipaat is a fast-growing professional training provider that is offering training in over 150 most sought-after tools and technologies. We have a learner base of 600,000 in over 32 countries and growing. For job assistance and placement we have direct tie-ups with 80+ MNCs. Key Features of Intellipaat Training: Instructor Led Training Self-Paced Training Exercise and project work Lifetime Access 143 Hrs of highly interactive instructor led training 178 Hrs of Self-Paced sessions with Lifetime access 286 Hrs of real-time projects after every module Lifetime access and free upgrade to latest version Support Lifetime 24*7 technical support and query resolution Get Certified Get global industry recognized certifications Job Assistance Job assistance through 80+ corporate tie-ups Flexi Scheduling Attend multiple batches for lifetime & stay updated. About the Course This is an Artificial Intelligence Engineer Master Course that is a comprehensive learning approach for mastering the domains of Artificial Intelligence, Data Science, Business Analytics, Business Intelligence, Python coding, and Deep Learning with TensorFlow. Upon completion of the training, you will be able to take on challenging roles in the artificial intelligence domain. Instructor Led Duration 143 Hrs Weekend Batch 3 Hrs/Session Self Paced Duration 178 Hrs

Why take this Course? Artificial intelligence is one of the hottest domains being heralded as the one with the ability to disrupt companies cutting across industry sectors. This Intellipaat Artificial Intelligence Engineer Master Course will equip you with all the necessary skills needed to take on challenging and exciting roles in the artificial intelligence, data science, business analytics, Python, R statistical computing domains and grab the best jobs in the industry at top-notch salaries. Course Curriculum Data Science with R Module /Topic Introduction to Data Science and Statistical Analytics Introduction to Data Science, Use cases The need for Business Analytics Data Science Life Cycle Different tools available for Data Science Introduction to R Installing R and R-Studio, R packages R Operators, if statements and loops (for, while, repeat, break, next), switch case Data Exploration, Data Wrangling, and R Data Structure Importing and Exporting data from an external source Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List) Functions, Apply Functions Data Visualization Bar Graph (Simple, Grouped, Stacked) Histogram, Pi Chart, Line Chart, Box (Whisker) Plot Scatter Plot Correlogram

Introduction to Statistics Terminologies of Statistics Measures of Centres, Measures of Spread, Probability Normal Distribution, Binary Distribution Hypothesis Testing, Chi-Square Test, ANOVA Predictive Modeling - 1 ( Linear Regression) Supervised Learning Linear Regression Bivariate Regression, Multiple Regression Analysis Correlation ( Positive, negative and neutral), Industrial Case Study Machine Learning Use-Cases Machine Learning Process Flow Machine Learning Categories Predictive Modeling - 2 ( Logistic Regression) Logistic Regression Decision Trees What are Classification and its use cases? What is Decision Tree? Algorithm for Decision Tree Induction Creating a Perfect Decision Tree Confusion Matrix Random Forest Random Forest What is Naive Bayes? Unsupervised learning What is Clustering & its Use Cases? What is K-means Clustering? What is Canopy Clustering? What is Hierarchical Clustering?

Association Analysis and Recommendation engine Market Basket Analysis (MBA), Association Rules Apriori Algorithm for MBA Introduction of Recommendation Engine Types of Recommendation User-Based and Item-Based, Recommendation Use-case Sentiment Analysis Introduction to Text Mining Introduction to Sentiment Setting up API Bridge, between R and Twitter Account Extracting Tweet from Twitter Acc, Scoring the tweet Time Series What is Time Series data? Time Series variables, Different components of Time Series data Visualize the data to identify Time Series Components Implement ARIMA model for forecasting, Exponential smoothing models Identifying different time series scenario based on which different Exponential Smoothing model can be applied Implement respective ETS model for forecasting Python for Data Science: Module / Topic Introduction to Python Introduction to Python Language, features The advantages of Python over other programming languages Python installation, Windows, Mac & Linux distribution for Anaconda Python Deploying Python IDE, basic Python commands, data types, variables, keywords and more Hands-on Exercises Installing Python Anaconda for the Windows, Linux, and Mac. Python language Basic Constructs

Built-in data types in Python, tabs, and spaces indentation Code comment Pound # character, variables, and names Python built-in data types, Numeric, int, float, complex, list tuple, set dict Containers, text sequence, exceptions, instances, classes, modules, Str(String) Ellipsis Object, Null Object, Ellipsis, Debug Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise, loop and control statements, while, for, if, break, else, continue Write your first Python program Write a Python Function (with and without parameters) Use Lambda expression Write a class, create a member function and a variable Create an object Write a for loop to print all odd numbers OOP programming in Python and database connection How to write OOP program in Python, connecting to a database Classes and objects in Python, OOPs paradigm Important concepts in OOP like polymorphism, inheritance, encapsulation Python functions, return types, and parameters Lambda expressions, connecting to the database and pulling the data Python file handling, exception handling How to open a file, read from a file Writing into a file, serializing and deserializing Python objects Resetting current position in a file, the shelve to over limitation of Pickle Exception in Python, raising in Python How to catch an exception? Opening a text file, reading its contents Writing a new line in an opened file, using pickle to serialize the Python object De-serializing objects, raising an exception and catching it

NumPy for mathematical computing Introduction to arrays and matrices Indexing of array, datatypes, broadcasting of array math, standard deviation Conditional probability, correlation and covariance How to import NumPy module Creating array using ND-array Calculating standard deviation on an array of numbers Calculating correlation between two variables SciPy for scientific computing Introduction to SciPy and its functions Building on top of NumPy, cluster, linalg, signal, optimize, integrate, sub packages SciPy with Bayes Theorem Importing of SciPy Applying the Bayes theorem on the given dataset Matplotlib for data visualization How to plot graph and chart with Python Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram Pandas for data analysis and machine learning Introduction to Python dataframes Importing data from JSON, CSV, Excel, SQL database NumPy array to dataframe Various data operations like selecting, filtering, sorting, viewing, joining, combining How to handle missing values, time series analysis, linear regression? Working on importing data from JSON files Selecting record by a group, applying a filter on top, viewing records Analyzing with linear regression, and creation of time series Scikit-Learn for Natural Language Processing What is natural language processing? Working with NLP on text data, setting up the environment using Jupyter Notebook, analyzing the sentence The Scikit-Learn machine learning algorithms, bags of words model Extracting feature from text Setting up the Jupyter notebook environment, loading of a dataset in Jupyter, algorithms in Scikit-Learn package for performing machine learning techniques, training a model to search a grid

Searching a grid, model training, multiple parameters, building of a pipeline Web scraping with Python Introduction to web scraping in Python The various web scraping libraries, beautifulsoup, Scrapy Python packages Installing of beautifulsoup, installing Python parser lxml Creating soup object with input HTML, searching of tree Full or partial parsing, output print, searching the tree Installation of Beautiful soup and lxml Python parser Making a soup object with input HTML file, navigating using Py objects in soup tree Python deployed for Hadoop Introduction to Python for Hadoop The basics of the Hadoop ecosystem, Hadoop common The architecture of MapReduce and HDFS Deploying Python coding for MapReduce jobs on Hadoop framework How to write a MapReduce job with Python Connecting to the Hadoop framework and performing the tasks Python for Apache Spark coding Introduction to Apache Spark, the importance of RDD, the Spark libraries Deploying Spark code with Python The machine learning library of Spark MLlib Deploying Spark MLlib for classification, clustering, and regression How to implement Python in a sandbox Working with the HDFS file system. Machine Learning: Introduction to Machine Learning Module / Topic The history of artificial intelligence How machine learning fits into the picture

Importance of machine learning The various algorithms in machine learning How machine learning is changing various industries Various techniques of Machine Learning The various techniques of machine learning like supervised, unsupervised and reinforcement learning The various aspects of bias and variance trade-off Representation learning Mathematics of Machine Learning The fundamentals of machine learning mathematics The various algorithms used in machine learning The concepts of statistics, various aspects of calculus Probability and statistics Preprocessing of data Preparing data for machine learning The aspects of feature engineering, dimensionality reduction Data sets and feature scaling Supervised learning techniques The importance of supervised learning in machine learning Parametric and non-parametric algorithms, neural networks Kernels for pattern analysis Introduction to regression The various types of regressions Linear regression, random forests, gradient descent Decision tree regression, regularization Techniques of classification Introduction of classification The various techniques of classification like logistic regression Support Vector Machines, K-Nearest Neighbour, Naïve Bayes

Decision Tree Classifier, Random Forest Classifier Unsupervised Learning The introduction to unsupervised learning The various techniques used in unsupervised learning like clustering K-Means Clustering Introduction to Deep Learning How deep learning differs from machine learning Artificial neural networks with multiple layers Introduction to TensorFlow for building neural networks Back-propagation algorithm in neutral networks Artificial Intelligence and Deep Learning with Tensorflow: Module / Topic Introduction to Deep Learning & Neural Networks The domain of machine learning and its implications to the artificial intelligence sector The advantages of machine learning over other conventional methodologies Introduction to Deep Learning within machine learning How it differs from all others methods of machine learning Training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning The algorithms used in these types of learning Introduction to AI, Introduction to Neural Networks Supervised Learning with Neural Networks The concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing, Hidden Markov Model Multi-layered Neural Networks Introduction to Multi-Layer Network The concept of Deep neural networks, Regularization

Module / Topic Multi-layer perceptron, capacity and over fitting, neural network hyperparameters, logic gates The various activation functions in neural networks like Sigmoid, ReLu and Softmax, hyperbolic functions Backpropagation, convergence, forward propagation, overfitting, hyperparameters Training of neural networks The various techniques used in training of artificial neural networks Gradient descent rule, perceptron learning rule, tuning learning rate, stochastic process Optimization techniques, regularization techniques, regression techniques Lasso L1, Ridge L2 Vanishing gradients, transfer learning Unsupervised pre-training, Xavier initialization, vanishing gradients Deep Learning Libraries How Deep Learning Works, Activation Functions Illustrate Perceptron, Training a Perceptron Important Parameters of Perceptron, Multi-layer Perceptron What is Tensorflow? Introduction to TensorFlow open source software library for designing Building and training Deep Learning models Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google, Tensorflow code-basics Graph Visualization, Constants, Placeholders, Variables Step by Step Use-Case Implementation, Keras Introduction to Keras API Keras high-level neural network for working on top of TensorFlow Defining complex multi-output models Composing models using Keras, sequential and functional composition, batch normalization Deploying Keras with TensorBoard Neural network training process customization

TFLearn API for TensorFLow Module / Topic Implementing neural networks using TFLearn API Defining and composing models using TFLearn Deploying TensorBoard with TFLearn DNN: Deep Neural Networks Mapping the human mind with Deep Neural Networks The various building blocks of Artificial Neural Networks The architecture of DNN, its building blocks The concept of reinforcement learning in DNN The various parameters, layers Activation functions and optimization algorithms in DNN CNN: Convolutional Neural Networks Introduction to CNNs, CNNs Application The architecture of a CNN, Convolution and Pooling layers in a CNN Understanding and Visualizing a CNN Transfer Learning and Fine-tuning Convolutional Neural Networks, feature maps, Kernel filter, pooling Deploying convolutional neural network in TensorFlow RNN: Recurrent Neural Networks Intro to RNN Model, Application use cases of RNN Modeling sequences, Training RNNs with Backpropagation Long Short-Term Memory (LSTM) Recursive Neural Tensor Network Theory Recurrent Neural Network Model, basic RNN cell, unfolded RNN Training of RNN, dynamic RNN, time-series predictions GPU in Deep Learning Introduction to GPUs and how they differ from CPUs The importance of GPUs in training Deep Learning Networks The forward pass and backward pass training technique The GPU constituent with simpler core and concurrent hardware

Module / Topic Autoencoders & Restricted Boltzmann Machine (RBM) Introduction to RBM and autoencoders Deploying it for deep neural networks, collaborative filtering using RBM Features of autoencoders, applications of autoencoders Chatbots Automated conversation bots using one of the descriptive techniques o IBM Watson o Google API.AI o Microsoft s Luis o Amazon Lex o Generative o Open-Close Domain Bots o The sequence to Sequence model (LSTM) Learning Path

Project Work Data Science Projects: Project 1: Cold Start Problem in Data Science Industry: E-commerce Problem Statement: How to build a recommender system without the historical data available Topics: This project involves an understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques. Highlight: Algorithms for Recommender Ways of Recommendation Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation Complete mastery in working with the Cold Start Problem. Project 2: Recommendation for Movie, Summary Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting rating, learning about user preference and so on. You will exclusively work on data related to user details, movie details, and others. The main components of the project include the following: Recommendation for movie Two Types of Predictions Rating Prediction, Item Prediction Important Approaches: Memory-Based and Model-Based Knowing User Based Methods in K-Nearest Neighbor Understanding Item Based Method Matrix Factorization Decomposition of Singular Value Data Science Project discussion Collaboration Filtering Business Variables Overview

Project 3: Making sense of customer online buying pattern Industry: e-commerce Problem Statement: An e-commerce company wants to know how to deploy targeted selling to its customers Description: This Data Science project involves learning from the customer buying habits and selling them the products that they need. As part of the project, you will aggregate, cleanse, transform and load the data of customer buying history. Then you will deploy statistical analysis, predictive modeling and create profiles of customers to implement targeted selling to them. Highlights: Decision Tree for classification of customers R computing for statistical analysis Techniques for predictive modeling Project 4: Fraud Detection in Banking System Industry: Banking and Finance Problem Statement: A major bank wants to deploy data science to detect fraudulent activities and take remedial actions before it is too late Description: This data science project will help you understand how you can look for fraudulent activities in a banking ecosystem. You will work with banking transactional data, look for outliers in the data, classify this data based on various parameters, apply statistics and come up with inferences to look for rogue transactions and mitigate the risk before it is too late. Highlights: Data aggregation and analysis K-Means Clustering using R program Visualizing the data for inferences Case Study The Market Basket Analysis (MBA) case study This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real-world scenarios like a supermarket shopping cart and so on.

Python for Data Science Projects Project 1: Python Web Scraping for Data Science In this project, you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function, and keyword argument. Project 2: Create a password generator Objective: To generate a password using Python code which would be tough to guess Requirements: To generate a password that is 8-12 characters long Password contains at least two special characters The password doesn t start with a special character Project 3: Impact of pre-paid plans on the preferences of investors Domain: Finance Objective: The project aims to find the most impacting factors in preferences of the pre-paid model, also identifies which are all the variables highly correlated with impacting factors Requirements: To identify the various reasons for Pre-paid model preference and non-preference among the investors. And also understand the penetration of the Pre-paid model in the brokerage firms To identify the Pre-paid scheme advantages and disadvantages and also identify the brand wise market share In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale Project 4: Machine Learning Prediction of stock prices Domain: Stock Market Objective: This project focuses on Machine Learning by creating a predictive data model to predict future stock prices Requirements:

Quantitative Value Investing: Predict 6-month price movements based fundamental indicators from companies quarterly reports Forecasting: Build time series models on the delta between implied and actual volatility Predict 6-month price movements based fundamental indicators from companies quarterly reports Build time series models on the delta between implied and actual volatility? Project 5: Server logs/firewall logs Objective: This includes the process of loading the server logs into the cluster using Flume. It can then be refined using Pig Script, Ambari, and HCatlog. You can then visualize it using elastic search and excel. This project task includes: Server logs Potential uses of server log data Pig script Firewall logs Work flow editor AI Deep Learning Projects Project 1: Image recognition with TensorFlow Industry: Internet Search Problem Statement: Building a robust deep learning model to recognize the right object on the internet depending on the user search for the image. Description: In this project, you will learn how to build Convolutional Neural Network using Google TensorFlow. You will do visualization of images using training, providing input images, losses, and distributions of activations and gradients. You will learn to break each image into manageable tiles and input it to the Convolutional Neural Network for the desired result. Highlights: Constructing Convolutional Neural Network using TensorFlow Convolutional, Dense & Pooling layers of CNNs Filtering the images based on user queries.

Project 2: Handwriting recognition with Neural Networks Industry: General Problem Statement: Building an artificial Intelligence network with TensorFlow to identify the handwriting based on the input training data. Topic: You will build an artificial intelligence model for training the neural network to recognize the handwriting. The various layers of a neural network like input, hidden and output layers along with their functions will be clear. Implementing back-propagation for calculating the error of each neuron used with a gradient-based optimizer is explained. Highlights: TensorFlow to build Neural Networks Choosing the right number of hidden layers The importance of back propagation. Project 3: Building an AI-based chatbot Industry: E-commerce Description: This project involves building the chatbots using Artificial Intelligence and Google TensorFlow. Problem Statement: Understanding the customer needs and offering the right services through Artificial Intelligence chatbot. You will learn how to create the right artificial neural network with the right amount of layers to ensure the customer queries are comprehensible to the Artificial Intelligence chatbot. This will help to understand natural language processing, understanding beyond keywords, data parsing and providing the right solutions. Highlights: Breaking user queries into components Building neural networks with TensorFlow Natural language processing. Project 4: E-commerce product recommendation Industry: E-commerce Problem Statement: Recommending the right projects to customers by artificial intelligence Description: This project involves working with recommender systems to provide the right product recommendation to customers with TensorFlow. You will learn how to use Artificial Intelligence to check for user past buying habits, find out what are the products that go hand-in-hand, and recommend the best products for a particular product.

Highlights: Building neural networks with TensorFlow Looking at huge amounts of data & gaining insights Building recommendation engine with TensorFlow Graph. Intellipaat Job Assistance Program Intellipaat is offering comprehensive job assistance to all the learners who have successfully completed the training. A learner will be considered to have successfully completed the training if he/she finishes all the exercises, case studies, projects and gets a minimum of 60% marks in the Intellipaat qualifying exam. Intellipaat has exclusive tie-ups with over 80 MNCs for placement. All the resumes of eligible candidates will be forwarded to the Intellipaat job assistance partners. Once there is a relevant opening in any of the companies, you will get a call directly for the job interview from that particular company. Frequently Asked Questions: Q 1. What is the criterion for availing the Intellipaat job assistance program? Ans. All Intellipaat learners who have successfully completed the training post April 2017 are directly eligible for the Intellipaat job assistance program. Q 2. Which are the companies that I can get placed in? Ans. We have exclusive tie-ups with MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, and more. So you have the opportunity to get placed in these top global companies. Q 3. Does Intellipaat help learners to crack the job interviews? Ans. Intellipaat has an exclusive section which includes the top interview questions asked in top MNCs for most of the technologies and tools for which we provide training. Other than that our support and technical team can also help you in this regard.

Q 4. Do I need to have prior industry experience for getting an interview call? Ans. There is no need to have any prior industry experience for getting an interview call. In fact, the successful completion of the Intellipaat certification training is equivalent to six months of industry experience. This is definitely an added advantage when you are attending an interview. Q 5. What is the job location that I will get? Ans. Intellipaat will try to get you a job in your same location provided such a vacancy exists in that location. Q 6. Which is the domain that I will get placed in? Ans. Depending on the Intellipaat certification training you have successfully completed, you will be placed in the same domain. Q 7. Is there any fee for the Intellipaat placement assistance? Ans. Intellipaat does not charge any fees as part of the placement assistance program. Q 8. If I don t get a job in the first attempt, can I get another chance? Ans. Definitely, yes. Your resume will be in our database and we will circulate it to our MNC partners until you get a job. So there is no upper limit to the number of job interviews you can attend. Q 9. Does Intellipaat guarantee a job through its job assistance program? Ans. Intellipaat does not guarantee any job through the job assistance program. However, we will definitely offer you full assistance by circulating your resume among our affiliate partners. Q 10. What is the salary that I will be getting once I get the job? Ans. Your salary will be directly commensurate with your abilities and the prevailing industry standards.

What makes us who we are? I want to talk about the rich LMS that Intellipaat Artificial Intelligence training offered. The extensive set of PPTs, PDFs, and course material were of the highest quality and due to this my learning with Intellipaat was excellent. -Shreyash Limbhetwala I had taken the Artificial Intelligence master program. Since there are so many technologies involved in the AI course, getting your query resolved at the right time becomes the most important aspect. But with Intellipaat there was no such problem as all my queries were resolved in less than 24 hours. - Giri Karnal The Intellipaat AI training videos really made me excited about studying. They were so elaborate and so professionally created that I could learn AI from the comfort of my home thanks to those learner-friendly videos. I am grateful to Intellipaat. - Nitesh Kumar