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Artificial Intelligence Masters Program Training Course

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  • Beginner & Advanced level Classes.
  • Hands-On Learning in Artificial Intelligence.
  • Best Practice for interview Preparation Techniques in AI.
  • Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
  • Affordable Fees with Best Curriculum Designed by Industrial Artificial Intelligence Expert.
  • Delivered by 9+ years of Artificial Intelligence Certified Expert | 15485+ Students Trained & 350+ Recruiting Clients.
  • Next Artificial Intelligence Masters Program Training Batch to Begin this week – Enroll Your Name Now!

  • 130+ Hrs Hands On Training
  • 6 Live Projects For Hands-On Learning
  • 37+ Practical Assignments
  • 24/7 Students
  • Course Syllabus Artificial Intelligence

    ₹ 16000
  • Course SyllabusData Science with Python

    ₹ 22000
  • Course Syllabus Machine Learning

    ₹ 16000
  • Course SyllabusDeep Learning with Keras and TensorFlow

    ₹ 18000
  • Course SyllabusAdvanced Deep Learning

    ₹ 16000
  • Course SyllabusNatural Language Processing (NLP)

    ₹ 12000
  • Course SyllabusAI capstone project

    ₹ 14000
  • Total Amount

    ₹ 114000
  • Discount Offered

    ₹ 51300
  • Amount To Pay

    ₹ 62700

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Upcoming Batches

14- JUN- 2021
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

16- JUN- 2021
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

19- JUN- 2021
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

19- JUN- 2021
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

Can't find a batch? Pick your own schedule

Outline of Artificial Intelligence Masters Program

  • We train students for interviews and Offer Placements in top corporate companies.
  • Suitable for Graduates and Experienced Candidates from any Technical Background
  • You will only gain knowledge of Artifical Intelligence and Advance tools, but also gain exposure to Industry best practices, Aptitude & SoftSkills
  • Experienced Trainers and sophiticated Lab Facility
  • IBM Certification Guidance Support with Exam Dumps
  • For Corporate, we act as one stop recruiting partner. We provide right skilled candidates who are productive right from day one
  • Guidance for Resume & Interviews Preparation Support
  • Learning Concepts: AI- Artifical Intelligence Concepts, Data Science with Python, Machine Learning, Deep Learning with Keras and TensorFlow, Advanced Deep Learning and Computer Vision etc
  • START YOUR CAREER WITH ARTIFICAL INTELLIGENCE COURSE THAT GETS YOU A JOB OF UPTO 5 LACS IN JUST 60 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

This is How ACTE Students Prepare for Better Jobs

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About Artificial Intelligence Masters Program Training Course

We would do whatever it takes to make this a definitive Artificial Intelligence Masters Program course. This would be your unique destination to learn Artificial Intelligence Masters Program.

Learning Artificial Intelligence Masters Program can help open up many opportunities for your career. It is a great skill-set to have as many roles in the job market requires proficiency in AI Masters. Mastering Artificial Intelligence Masters Program can help you get started with your career in IT Companies like Paypal, Capgemini, Accenture, Mphasis, CTS and MindLabs, etc, are all hiring Intelligence Designer.

This course will not only cover the core issues but will also cover many more advanced topics. This course is going to be one of the most comprehensive courses on ACTE. AI Concepts, Data Science with Python, Machine Learning, Deep Learning with Keras and TensorFlow, Advanced Deep Learning and Computer Vision; THERE IS NO PROBLEM. Everything is covered.

Best Job Oriented Topics Covered
  • Concepts of AI

    Natural Language Processing (NLP)

    Datascience concepts

  • Data Science with Python

    Deep Learning with Keras and TensorFlow

    AI capstone project

  • Machine Learning Concepts

    Advanced Deep Learning and Computer Vision

    Python Packages

Artificial Intelligence has opportunities with high pay, a growing number of intriguing sub-fields, and the ability to work with life-changing technology daily. Specific jobs that use AI are software engineers, data analysts, and roboticists.

Artificial intelligence is impacting the long run of nearly every business. It acted because the driver of rising technologies like huge information, robotics, and IoT, and it'll continue as a technological originator for the predictable future.

AI technology permits the capability of understanding, reasoning, planning, communication, and perception – to be undertaken by software systems more and more effectively, expeditiously, and at low prices. Applications of AIpowered laptop vision are going to be significantly vital within the transport sector.

We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for developer. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

AI works by combining giant amounts of knowledge with quick, repetitive processes and intelligent algorithms, permitting the software package to be told mechanically from patterns or options within the knowledge.

  • Concepts of AI/li>
  • Data Science with Python
  • Machine Learning Concepts
  • Natural Language Processing (NLP)
  • Deep Learning with Keras and TensorFlow
  • Advanced Deep Learning and Computer Vision.

Artificial Intelligence certified Intelligence Designer should know at least know the basics of programming languages like Python etc. Knowledge of various technology like AI Algorithms, Machine Learning, etc.

Our courseware is designed to give a hands-on approach to the students in Artificial Intelligence Masters Program. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.

Anyone from computer or Engineering background holder can start to learn the AI Master Program. Artificial Intelligence necessity for information of a programming (computer) language like Python then etc. Being a Freshers in AI is not about familiarity; it's about an intuitive and deep understanding all the technologies related to AI practices and paradigms.

Three-Four months is long enough to learn a considerable amount of Artificial Intelligence Masters Program. If you are already able to fluently program in another language, then 2 months would be a generous amount of time to learn enough Artificial Intelligence Masters Program to meaningfully contribute in a professional capacity.

Top reasons to consider a career in Artificial Intelligence Masters Program?

  • The Demand for Artificial Intelligence Masters Program is high.
  • The average salary of Freshers Masters developers in India is around 8 LPA.
  • Creative Flexibility.
  • You know about multiple aspects of development.
  • Better Productivity.
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Key Features

ACTE offers rtificial Intelligence Masters Program Training in more than 27+ branches with expert trainers. Here are the key features,

  • 160 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.

Artificial Intelligence Masters Program Course Content

Syllabus of Artificial Intelligence
Module 1: Introduction to Data Science
  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types
Module 2: Introduction to Python
  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
Module 3: Python Basics
  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels
Module 4: Python Packages
  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
Module 5: Importing Data
  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file
Module 6: Manipulating Data
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
Module 7: Statistics Basics
  • Central Tendency
  • Probability Basics
  • Standard Deviation
  • Bias variance Trade off
  • Distance metrics
  • Outlier analysis
  • Missing Value treatment
  • Correlation
Module 8: Error Metrics
  • Classification
  • Regression
Module 9: Machine Learning
  • Supervised Learning
  • Linear Regression
  • Logistic regression
Module 10: Unsupervised Learning
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
Module 11: SVM
  • Support Vectors
  • Hyperplanes
  • 2-D Case
  • Linear Hyperplane
Module 12: SVM Kernal
  • Linear
  • Radial
  • polynomial
Module 13: Other Machine Learning algorithms
  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest
Module 14: ARTIFICIAL INTELLIGENCE
  • Perceptron
  • Multi-Layer perceptron
  • Markov Decision Process
  • Logical Agent & First Order Logic
  • AL Applications
Module 15: Deep Learning Algorithms
  • CNN – Convolutional Neural Network
  • RNN – Recurrent Neural Network
  • ANN – Artificial Neural Network
Module 16: Introduction to NLP
  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization
Module 17: Text to Features
  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s
Module 18: Tasks of NLP
  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching
Syllabus of Data Science with Python Course
Module 1: Introduction to Data Science
  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types
Module 2: Introduction to Python
  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
Module 3: Python Basics
  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels
Module 4: Python Packages
  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
Module 5: Importing data
  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file
Module 6: Manipulating Data
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
Module 7: Statistics Basics
  • Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
  • Probability Basics
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
  • Standard Deviation
  • Data deviation & distribution
  • Variance
  • Bias variance Trade off
  • Underfitting
  • Overfitting
  • Distance metrics
  • Euclidean Distance
  • Manhattan Distance
  • Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • catter plot
  • Cook’s Distance
  • Missing Value treatments
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
  • Correlation
  • Pearson correlation
  • Positive & Negative correlation
  • Error Metrics
  • Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • Regression
  • MSE
  • RMSE
  • MAPE
Module 8: Machine Learning Module 9: Supervised Learning
  • Linear Regression
  • Linear Equation
  • Slope<
  • Intercept
  • R square value
  • Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff
Module 10: Unsupervised Learning
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
Module 11: Other Machine Learning algorithms
  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest
Syllabus of Machine Learning Course
Module 1: Introduction to Data Analytics
  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R
Module 2: Introduction to R programming
  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors
Module 3: Data Manipulation in R
  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions
Module 4: Data Import techniques in R
  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping
Module 5: Exploratory data Analysis
  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs
Module 6: Basics of Statistics & Linear & Logistic Regression
  • Basics of Statistics
  • Inferencial statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression
Module 7: Data Mining: Clustering techniques, Regression & Classification
  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering
Module 8: Anova & Sentiment Analysis
  • Anova
  • Sentiment Analysis
Module 9: Data Mining: Decision Trees and Random Forest
  • Decision Tree
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest
Module 10: Project work
Syllabus of Deep Learning Course with TensorFlow Training Course
Module 1: 1. Essential Programming
  • 1. Introduction to Deep Learning
  • 2. Introduction to Numpy
  • 3. Introduction to Tensorflow and Keras
Module 2: Essential basics of Linear Algebra
  • 1. Solution of Equations, row and column Interpretation
  • 2. Vector Space Properties
  • 3. Partial Derivative of Polynomial and Two conditions for Local Minima
  • 4. Physical Interpretation of gradient (Direction of Maximum Change)
  • 5. Matrix Vector Multiplication
  • 6. EVD and interpretation of Eighen Vectors
  • 7. Linear Independence and Rank of Matrix
  • 8. Orthonormal Matrices, Projection Matrices, Vandemonde Matrix, Markov Matrix, Symmetric, Block Diagonal
Module 3: Selected topics of Machine Learning
  • 1. Intuition behind Linear Regression, classification
  • 2. Grid Search
  • 3. Gradient Descent
  • 4. Training Pipeline
  • 5. Metrics ROC Curve, Precision Recall Curve
  • 6. Calculating Entropy
Module 4: Basics of Neural Network
  • 1. Evolution of Perceptrons, Hebbs Principle, Cat Experiment
  • 2. Single layer NN
  • 3. Tensorflow Code
  • 4. Multilayer NN
  • 5. Back propagation, Dynamic Programming
  • 6. Mathematical Take on NN
  • 7. Function Approximator
  • 8. Link with Linear Regression
  • 9. Dropout and Activation
  • 10. Optimizers and Loss Functions
Module 5: Introduction to Convolutional Neural Network
  • 1. 1D and 2D Convolution
  • 2. Why CNN for Images and speech?
  • 3. Convolution Layer
  • 4. Coding Convolution Layer
  • 5. Learning Sharpening using single convolution Layer in Tensor-Flow
Module 6: Different Layers in CNN pipeline
  • 1. Convolution
  • 2. Pooling
  • 3. Activation
  • 4. Dropout
  • 5. Batch Normalization
  • 6.Object Classification
  • 7. Creating Batch in Tensorflow and Normalize
  • 8. Training MNIST and CIFAR datasets
  • 9. Understanding a pre-trained Inception Architecture
  • 10. Input Augmentation Techniques for Images
Module 7: Transfer Learning
  • 1. Finetuning last layers of CNN Model
  • 2. Selecting appropriate Loss
  • 3. Adding a new class in the last Layer
  • 4. Making a model Fully Convolutional for Deployment
  • 5. Finetune Imagenet for Cats vs Dog Classification.
Module 8: Object Detection and Localization
  • 1. Different types of problem in Objects
  • 2. Difficulties in Object Detection and Localization
  • 3. Fast RCNN
  • 4. Faster RCNN
  • 5. YOLO v1-v3
  • 6. SSD
  • 7. MobileNet
Module 9: Autoencoders
  • 1. Image Compression Simple Autoencoder
  • 2. Denoising Autoencoder
  • 3. Variational Autoencoder and Reparematrization Trick
  • 4. Robust Word Embedding using Variational Autoencoder
Module 10: Time Series Modelling
  • 1. Evolution of Recurrent Structures
  • 2. LSTM, RNN, GRU, Bi-RNN, Time-Dense
  • 3. Learning a Sine Wave using RNN in Tensorflow
  • 4. Creating Autocomplete for Harry Potter in Tensorflow
Module 11: GANs
  • 1. Generative vs Discrimative Models
  • 2. Theory of GAN
  • 3. Simple Distribution Generator in Tensorflow using MCMC (Markov Chain Monte Carlo)
  • 4. DCGAN,WGANs for Images
  • 5. InfoGANs, CycleGANs and Progressive GANs
  • 6. Creating a GAN for generating Manga Art
Module 12: Model Free Approaches in Reinforcement Learning
  • 1. Model Free Prediction
  • 2. Monte Carlo Prediction and TD Learning
  • 3. Model Free Control with REINFORCE and SARSA Learning
  • 4. Assignment : Implementation of REINFORCE and SARSA Learning in Gridworld
  • 5. Off policy vs On Policy Learning
  • 6. Importance Sampling for Off Policy Learning
  • 7. Q Learning
Module 13: Behavioral Cloning and Deep Q Learning
  • 1. Understanding Deep Learning as Function Approximator
  • 2. Theory of Behavioral Cloning and Deep Q Learning
  • 3. Revisiting Point Collector Example in Unity and
  • 4. Assignment : Training Cartpole Example via Deep Q Learning
Module 14: Deep Learning in Action
  • 1. Face Detection using Yolo-v3
  • 2. Building Autocomplete Feature using RNNs
  • 3. Real-time Depth Prediction and Pose Estimation
  • 4. How is Deep Learning used in Autonomous Driver Assistant systems
  • 5. Tips and Tricks for scaling and easy Deployment of Deep Learning Models
Advanced Deep Learning
Module 1: Introduction to Data Science
  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & it’s types
Module 2: Introduction to Python
  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
Module 3: Python Basics
  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels
Module 4: Python Packages
  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
Module 5: Importing Data
  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to csv file
Module 6: Manipulating Data
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
Module 7: Statistics Basics
  • Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
  • Probability Basics
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
  • Standard Deviation
  • Data deviation & distribution
  • Variance
  • Bias variance Trade off
  • Underfitting
  • Overfitting
  • Distance metrics
  • Euclidean Distance
  • Manhattan Distance
  • Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
  • Missing Value treatment
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
  • Correlation
  • Pearson correlation
  • Positive & Negative correlation
Module 8: Error Metrics
  • Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
  • Regression
  • MSE
  • RMSE
  • MAPE
Module 9: Machine Learning
  • Supervised Learning
  • Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
  • Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure Bias Variance Tradeoff
  • ROC curve
  • Bias Variance Tradeoff
  • Unsupervised Learning
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
  • SVM
  • Support Vectors
  • Hyperplanes
  • 2-D Case
  • Linear Hyperplane
  • SVM Kernal
  • Linear
  • Radial
  • polynomial
  • Other Machine Learning algorithms
  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest
Module 10: ARTIFICIAL INTELLIGENCE
  • AI Introduction
  • Perceptron
  • Multi-Layer perceptron
  • Markov Decision Process
  • Logical Agent & First Order Logic
  • AL Applications
Module 11: Deep Learning Algorithms
  • CNN – Convolutional Neural Network
  • RNN – Recurrent Neural Network
  • ANN – Artificial Neural Network
  • Introduction to NLP
  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization
  • Text to Features (Feature Engineering)
  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s
  • Tasks of NLP
  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching
Module 12: Design Effective Reports
  • Enhance report design
  • Add report objects to enhance design
  • Format data and report objects
  • Add a background image to a report
  • Add row numbers to a report
Module 13: Customize Reports with Conditional Formatting
  • Create multi-lingual reports
  • Highlight exceptional data
  • Show and hide data
  • Conditionally render objects in reports
Module 14: Analysis Studio
  • Analysis Studio Fundamentals
  • Nest Data in Crosstabs in Analysis Studio
  • Create Analysis with Multiple filter
  • Reusable analysis
  • Build Advanced Crosstabs in Analysis Studio
  • Focus with Filters in Analysis Studio
  • Creating reports from cubes
  • Drill down and drill up
Module 15: Event Studio
  • Introduction to Event Studio
  • Create an agent
  • Add tasks to an agent
  • Run an agent through its lifecycle
  • Schedule an agent
Module 16: Business Insight
  • Introdcution to Dashboards
  • Create Dashboard
  • Types of Filter-Value, Slider and advanced filter
  • Overview of RSS Feed and web Page
  • Content Pane
  • Create Widgets
  • Sort, Filter and Calculate data
  • Hands on
Module 17: Business Insight Advanced
  • Overview of Business Intelligence Advance level
  • Create Different types of Reports
  • Reporting Styles and filters
  • Create dashboard objects
  • Summarize data and Create Calculations
  • Dispatcher and Services
Module 18: Dispatcher in detail
  • All Services
  • Properties of Services
Syllabus of Natural Language Processing (NLP) Training Course
Module 1: Introduction to NLP and Deep Learning
  • 1. Overview of Natural Language Processing
  • 2. Machine learning methods
  • 3. Cutting-edge deep learning methods
Module 2: Neural models for machine translation and conversation
  • 1. Introduction to Statistical Machine Translation
  • 2. Introduction to neural models
  • 3. Introduction to neural models for translation and conversation
Module 3: Deep Semantic Similarity Models (DSSM)
  • 1. Introduction to Deep Semantic Similarity Model (DSSM) and its applications.
Module 4: Natural Language Understanding
  • 1. Introduction to methods applied in Natural Language Understanding
  • 2. Continuous word representations method
  • 3. Neural knowledge base embedding method
Module 5:Deep reinforcement learning in NLP
  • 1. Introduction to deep reinforcement learning techniques applied in NLP
Module 6: Vision-Language Multimodal Intelligence
  • 1. Neural models applied in Image captioning
  • 2. Neural models applied in visual question answering
AI Capstone Project
Module 1: Exploratory Data Analysis
  • Exploratory Data Analysis
Module 2: Model Building and fitting
  • Model Building and fitting
Module 3: Unsupervised learning
  • Unsupervised learning
Module 4: Representing results
  • Representing results
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Hands-on Live Projects in Aritificial Intelligence

Project 1
House Price Predicting System

This project have the dataset contain the price of a house in different areas and get the data from varies resources.

Project 2
Customer Recommendation Application

It will browsing history of the customer for your data and the platform in enhancing its income tremendously thanks to customer.

Project 3
Facial Emotion Recognition and Detection

It can detect the human emotions in real-time, including happy, sad, angry, afraid, surprise, disgust, and neutral.

Project 4
Voice-based Virtual Assistant System

It is a handy tool for simplifying everyday tasks like to search for items on the Web, to shop for products etc.

Our Top Hiring Partner for Placements

ACTE offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.

  • We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
  • We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
  • After completion of 70% rtificial Intelligence Masters Program training course content, we will arrange the interview calls to students & prepare them to F2F interaction
  • rtificial Intelligence Masters Program Trainers assist students in developing their resume matching the current industry needs
  • We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
  • We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

Be a Certified Expert in Artificial Intelligence Masters Program

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees.

Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

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About Experienced Artifical Intelligence Master Programming Trainers

  • Our Artificial Intelligence Master Program Course : Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
  • As all Trainers are AI domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
  • All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
  • Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
  • Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Artificial Intelligence training to the students.
  • We have received various prestigious awards for Artificial Intelligence Training recognized IT organizations.

Artificial Intelligence Course Reviews

Our ACTE Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.

NANCY RAMKUMAR

Studying

I had completed my rtificial Intelligence Masters Program with advance in by ACTE. The best place to excel your next phase of career. The trainers and faculty here were very friendly and comfortable. My trainer had explained each and every topics clearly and he won't move to the another topic until I was clear in it. Thanks to my trainer and ACTE for this wonderful opportunity!!

CHANDRAN KANNAN

Software Engineer

It is a great curse for beginners and for filling in some gaps for intermediate programmers. I wouldn't recommend it though for advanced programmers as the knowledge is rather basic and even at x2.0 speed it will become boring. Again, great course overall of beginners who want to get started with rtificial Intelligence Masters Program . Thanks!

AMAN ADITHYA

Software Engineer

This course is really great, it's very awesome that the course constantly get updated and you get answers in Q&A very fast, also instructor explanation is very good and clear, this is the only course you need to master artificial Intelligence Masters Program and Mr Venket does a really good job on that, the only con for me was that it didn't have much theory covered it went right to code without showing slides about how it works, but thankfully they are adding them now, so this is really a 5 star course, thank you ACTE for these amazing courses.

KEERTHANA V

Studying

One of the best Programming courses that i have taken in a long time, ACTE has put a lot of effort into making the course easy to learn, even for artificial Intelligence Masters Program Novice like me, which makes it truly a masterclass course to attend ,and the responses from Goran and others have been very helpful .

RAMYA PANDIAN

Software Testing

This Course is really a masterclass, covers from basic to advanced level. Would definitely recommend to anyone interested in learning artificial Intelligence Masters Program. All concepts explained in a well-structured course. Tim teaches the concepts really well with coding examples. Follow along with him on code, you will learn a lot on writing a professional code too!.. Thanks, ACTE & Team

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Artificial Intelligence Masters Program Training Course FAQs

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Call now: +9193833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    ACTE Gives Certificate For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Artificial Intelligence Masters Program Training Course in training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Artificial Intelligence Masters Program Training Course At ACTE?

  • Artificial Intelligence Masters Program Training Course Course in ACTE is designed & conducted by Artificial Intelligence Masters Program Training Course experts with 10+ years of experience in the Artificial Intelligence Masters Program domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Artificial Intelligence Masters Program Training Course batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Artificial Intelligence Masters Program Training Course. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at 93833 99991 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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