Artificial Intelligence Training

 

About Course

Learn About Artificial Intelligence Online Course

AI Training In Hyderabad which is currently being delivered by the “Lucidtechsystems” is widely preferred in relation to leveraging complete advanced & job oriented knowledge. The training experts here will help the students to gain an all-around knowledge of the advanced concepts of AI through hands-on practical based learning approach. Upskill your knowledge in the advanced & in-trend analytics technology of Artificial Intelligence with the aid of Sacrostect Services AI Course In Hyderabad. Interested aspirants can also avail our institutes advanced live instructor based AI Online Training program.

What Exactly Is Artificial Intelligence?
Artificial Intelligence is an advanced technology in the sphere of the analytics domain. It is also named by the Machine Intelligence that means it is a quiet contrast with natural intelligence by humans. Artificial Intelligence today is being implemented to automate tasks by making machines smarter allowing them to function in an intelligent manner without much of human interference.

Artificial learning is considered as the branch of computer science concerned with making computers behave like humans. So the Artificial Intelligence is the most trend technology with the great impact that how the user interacts with the internet. In the near future, it’s here to show the great effect and has good potential with a huge change in human interactions. Hence it is considered as the reproduction of human intelligence processes in the computer systems.
The major aspects of AI include
• Learning
• Reasoning
• Self-Correction

The Artificial Learning Training In Hyderabad by Sacrostect Services has been designed specially by domain experts covering all the essential topics about the modern Artificial Intelligence (AI) as well as including the representative applications of AI.
Why Take This Course?
The prime reasons for why to opt for a career in Artificial Intelligence technology are
• Artificial Intelligence (AI) is a technology which is increasingly prevalent in the present day’s the digital world
• It has innumerable applications over a wide range of industries from media, gaming to finance as well as in the field of robotics, quantum science, and medical diagnosis.
• The number of opportunities for employment in this ingenious domain are quite high
• The salary packages for AI professionals are also very high compared to the other IT & corporate working professionals
In this Artificial Intelligence, you will learn all the applications and basics of AI, including Probabilistic reasoning, machine learning, computer vision, robotics, and natural language processing.

Learning Modules Of Artificial Intelligence:
The aspirants who attain our Artificial Intelligence training will be able to gain complete end-to-end knowledge & hands-on working skill sets in this domain. The major set of learning outcomes of our AI Online Training include
• Understand the in-depth concepts behind Artificial Intelligence (AI)
• Learn about the codes come in combined and what lines mean
• Understand about creating the environment for self-driving car
• Understand the perfect procedure for building AI
• Earn fame in the workplace with handsome salary
• Learn to build AI which is adaptable to any environment in real life
• How to build Artificial Intelligence with no previous coding previous experience using Python

Topics of the Artificial Intelligence Training:
This Artificial Intelligence Course In Hyderabad will be covered with the following essential topics mentioned below::
• Introduction to Artificial Intelligence (AI)
• Statistics, uncertainty and Bayes network
• Past, present and future applications of AI technology
• Fundamentals of machine learning
• Planning and machine logic, how to add these systems
• Computer version and utilizing the image processing
• Integration with Robotics
• Information retrieval systems
• Face detection applications

Who Can Take This Course?
There is a great demand for the skilled experts in Artificial Intelligence across all over the huge industries. Anyone who is interested in Artificial Intelligence, Deep learning or Machine learning can opt for this course. We also recommend this course for the following professionals:

• Database management professionals
• Software analysts
• Robotic Scientist
• Statisticians
• Freshers and Graduates who are having the math skills

“LucidTechSystems the Best Trusted Institute Delivering AI Training In Hyderabad”

“LucidTechSystems” Artificial Intelligence Training In Hyderabad is especially focusing on those aspirants who are really passionate to work in this most trending & advancing the field of Artificial Intelligence. Our live instructor based AI Online Training will be the best fit for all those working professionals who are planning at making a career in this advanced technology. Interested aspirants can also get enrolled for our Free AI Demo In Hyderabad.

Benefits To You

LucidTechSystems is a Best training center for Artificial Intelligence AI Training given corporate trainings to different reputed companies. In Best Artificial Intelligence AI Training all sessions are teaching with examples and with real time scenarios. We provide all recordings for classes, materials, sample resumes, and other important stuff. We are helping in real time how approach job market, Resume preparation, Interview point of preparation, how to solve problem in projects in job environment, information about job market etc. Artificial Intelligence AI classroom Training in Hyderabad and online from anywhere

Our courses are carefully planned and are in accordance with the highly valued certification exams. By the time of course completion, our students will become industry ready Artificial Intelligence AI professionals capable to handle all the real-world challenges of this domain. Master the Artificial Intelligence AI concepts and become a Artificial Intelligence AI expert by enrolling in our Artificial Intelligence AI Course. Best Artificial Intelligence AI Online Training through worldwide like India, USA, Japan, UK, Malaysia, Singapore, Australia, Sweden, South Africa, and etc. LucidTechSystems providing corporate training worldwide depending on Company requirements with well experience real time experts

LucidTechSystems gives an opportunity to work on real-time projects which would be guided by our real-time trainers. A technical back end team would always be available to answer your quires at any point of time and will also assist you to arrange your training process.

Artificial Intelligence Course Content

Core topics of Artificial Intelligence Online Course

Introduction to Data Science Deep Learning & Artificial Intelligence

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence
• Limitations of Machine Learning

What is Deep Learning?
• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence
• What is Data Analysis
• What is Data Mining

What is Machine Learning?
Analytics vs Data Science
• Value Chain
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle
• Advantage of Deep Learning over Machine learning
• Reasons for Deep Learning
• Real-Life use cases of Deep Learning
• Review of Machine Learning

Data
• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data & Sources
• Data Quality & Changes
• Data Quality Issues
• Data Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

Big Data
• What is Big Data?
• 5 Vs of Big Data
• Big Data Architecture
• Big Data Technologies
• Big Data Challenge
• Big Data Requirements
• Big Data Distributed Computing & Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

Data Science Deep Dive
• What Data Science is
• Why Data Scientists are in demand
• What is a Data Product
• The growing need for Data Science
• Large Scale Analysis Cost vs Storage
• Data Science Skills
• Data Science Use Cases
• Data Science Project Life Cycle & Stages
• Data Acuqisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File format Conversions
• Annonymization

Python
• Python Overview
• About Interpreted Languages
• Advantages/Disadvantages of Python pydoc.
• Starting Python
• Interpreter PATH
• Using the Interpreter
• Running a Python Script
• Using Variables
• Keywords
• Built-in Functions
• StringsDifferent Literals
• Math Operators and Expressions
• Writing to the Screen
• String Formatting
• Command Line Parameters and Flow Control.
• Lists
• Tuples
• Indexing and Slicing
• Iterating through a Sequence
• Functions for all Sequences

Operators and Keywords for Sequences
• The xrange() function
• List Comprehensions
• Generator Expressions
• Dictionaries and Sets.

Numpy & Pandas
• Learning NumPy
• Introduction to Pandas
• Creating Data Frames
• GroupingSorting
• Plotting Data
• Creating Functions
• Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops
• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections
• Classes & OOPs

Statistics
• What is Statistics
• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python
Introduction
• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques

Clustering
• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study

Implementing Association rule mining
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Recommendation Use-case
• Case study

Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
• How to build Decision trees
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Decision Tree
• Confusion Matrix
• Case study

Random Forest Classifier
• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study

Naive Bayes Classifier.
• Case study

Project Discussion
Problem Statement and Analysis
• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.

Linear Regression
• Case study
• Introduction to Predictive Modeling
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression

Logistic Regression
• Case study
• Logistic Regression Overview
• Data Partitioning
• Univariate Analysis
• Bivariate Analysis
• Multicollinearity Analysis
• Model Building
• Model Validation
• Model Performance Assessment AUC & ROC curves
• Scorecard

Support Vector Machines
• Case Study
• Introduction to SVMs
• SVM History
• Vectors Overview
• Decision Surfaces
• Linear SVMs
• The Kernel Trick
• Non-Linear SVMs
• The Kernel SVM

Time Series Analysis
• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model
• 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 model for forecasting
• Visualizing and formatting Time Series data
• Plotting decomposed Time Series data plot
• Applying ARIMA and ETS model for Time Series forecasting
• Forecasting for given Time period
• Case Study

Machine Learning Project
Machine learning algorithms Python
• Various machine learning algorithms in Python
• Apply machine learning algorithms in Python

Feature Selection and Pre-processing
• How to select the right data
• Which are the best features to use
• Additional feature selection techniques
• A feature selection case study
• Preprocessing
• Preprocessing Scaling Techniques
• How to preprocess your data
• How to scale your data
• Feature Scaling Final Project

Which Algorithms perform best
• Highly efficient machine learning algorithms
• Bagging Decision Trees
• The power of ensembles
• Random Forest Ensemble technique
• Boosting – Adaboost
• Boosting ensemble stochastic gradient boosting
• A final ensemble technique

Model selection cross validation score
• Introduction Model Tuning
• Parameter Tuning GridSearchCV
• A second method to tune your algorithm
• How to automate machine learning
• Which ML algo should you choose
• How to compare machine learning algorithms in practice

Text Mining& NLP
• Sentimental Analysis
• Case study

PySpark and MLLib
• Introduction to Spark Core
• Spark Architecture
• Working with RDDs
• Introduction to PySpark
• Machine learning with PySpark – Mllib

Deep Learning & AI using Python
Deep Learning & AI
• Case Study
• Deep Learning Overview
• The Brain vs Neuron
• Introduction to Deep Learning

Introduction to Artificial Neural Networks
• The Detailed ANN
• The Activation Functions
• How do ANNs work & learn
• Gradient Descent
• Stochastic Gradient Descent
• Backpropogation
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Backpropagation – Learning Algorithm
• Understand Backpropagation – Using Neural Network Example
• MLP Digit-Classifier using TensorFlow
• Building a multi-layered perceptron for classification
• Why Deep Networks
• Why Deep Networks give better accuracy?
• Use-Case Implementation
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent

Convolutional Neural Networks
• Convolutional Operation
• Relu Layers
• What is Pooling vs Flattening
• Full Connection
• Softmax vs Cross Entropy
• ” Building a real world convolutional neural network
• for image classification”

What are RNNs – Introduction to RNNs
• Recurrent neural networks rnn
• LSTMs understanding LSTMs
• long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders
• Restricted Boltzmann Machine
• Applications of RBM
• Introduction to Autoencoders
• Autoencoders applications
• Understanding Autoencoders
• Building a Autoencoder model

Tensorflow with Python
• Introducing Tensorflow
• Introducing Tensorflow
• Why Tensorflow?
• What is tensorflow?
• Tensorflow as an Interface
• Tensorflow as an environment
• Tensors
• Computation Graph
• Installing Tensorflow
• Tensorflow training
• Prepare Data
• Tensor types
• Loss and Optimization
• Running tensorflow programs

Building Neural Networks using
Tensorflow
• Tensors
• Tensorflow data types
• CPU vs GPU vs TPU
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture
• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN

Deep Learning using
Tensorflow
• Deepening the network
• Images and Pixels
• How humans recognise images
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Debugging Neural Networks
• Visualising NN using Tensorflow
• Tensorboard

Transfer Learning using
Keras and TFLearn
• Transfer Learning Introduction
• Google Inception Model
• Retraining Google Inception with our own data demo
• Predicting new images
• Transfer Learning Summary
• Extending Tensorflow
• Keras
• TFLearn
• Keras vs TFLearn Comparison

LucidTechSystems offers advanced Artificial Intelligence AI interview questions and answers along with Artificial Intelligence AI resume samples. Take a free sample practice test before appearing in the certification to improve your chances of scoring high

Upon successful completion of the course program of Artificial Intelligence AI Online Training program from LucidTechSystems students can work towards availing a certification. This certification will act as a gateway for the rising career opportunities in this domain.