Artificial Intelligence Training

Artificial Intelligence Training Pre-Requisites: Stronghold on Mathematics, Strong experience of programming languages, Writing an algorithm for finding patterns and learning, Strong data analytics skills, Strong will learn machine learning languages.

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Artificial Intelligence Course Syllabus

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
  • 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
  • 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?
  • 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
  • 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 Acquisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File Format Conversions
  • Anonymization
  • 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
  • Strings Different 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
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets
  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • Grouping Sorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs
  • 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 value
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

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 stud

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

  • 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”
  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building an Autoencoder model
 
  • 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 ensorflow programs

 

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 NIST NN

 

  • 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
  •  

 

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

Our Happy Student Reviews!

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About Artificial Intelligence Training

SVR Technologies offers a wide range of online courses you can master any course or software you wish to, right from the comfort of your home.

After completion of the Artificial Intelligence Training course, what you’ll learn is:

  • Introduction to Artificial intelligence Training and intelligent agents, history of Artificial Intelligence
  • Building intelligent agents
  • Machine learning algorithms
  • Applications of AI (Natural Language Processing, Robotics/Vision)
  • Solving real AI problems through programming with Python
  • Introduction to AI and intelligent agents, history of Artificial intelligence Training.
  • Gain familiarity with basic approaches to problem-solving and inference and areas of  application.
  • Statistical and decision-theoretic modeling paradigm
  • Applications of AI (Natural Language Processing, Robotics/Vision)
  • Solving real AI problems via programming with Python
  • Clustering, retrieval, recommender systems, Apply regression, classification, and deep learning of Artificial Intelligence.
  • Gain familiarity with techniques for improving human learning and influencing human thought

The persons who are eligible to do this Artificial intelligence Training course are:

  • Robotics Engineer
  • Data Scientist (artificial intelligence training)
  • Business Analysts
  • Hadoop Developers
  • Python for Data Science
  • College Graduates

Project 1: See how banks like ICICI, HDFC Citigroup, and Bank of America, making use of Artificial Intelligence to stay ahead of the competition (artificial intelligence training)

Domain: Banking

Description: A Portuguese banking institution ran a marketing battle to convince customers of the potential to invest in a bank term deposit. Their marketing operations were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing battle.

Project 2: Determine how Stock Markets purchase on Artificial Intelligence and Machine Learning like NSE, BSE, NASDAQ to appear at a consumable data from complex datasets.

Domain: Stock Market

Description: As a part of the project, you need to import data using Yahoo data reader of the following companies: Microsoft, Yahoo, Apple, Amazon, and Google. Perform analytics fundamentals including daily operating return analysis, plotting closing price, plotting stock trade by volume, and using pair plot to show the correlation between all the stock.

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Artificial Intelligence CV & Interview

Even after the completion of the course(Artificial Intelligence Training), we will provide you some interview questions where you can concentrate on them.

Yeah, Even after the completion of the Artificial intelligence Training course, we will provide you some interview questions where you can concentrate on them.

We provide excellent study materials and customizable course curriculum to students for superior quality training and we also provide videos recordings which support throughout your career.

After this Artificial intelligence Training course, hands-on training gives you confidence that you go in an interview with leaving 3years of experience.

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Artificial Intelligence Certification

we will guide you and give you a clear picture of the certification procedure during the time of Artificial intelligence Training.

All the topics in Artificial Intelligence Training will cover during the course. We provide question banks that will be helpful for you to attempt for certification.

Even after completion, of course- Artificial Intelligence Training, you can approach a trainer if you have any doubts regarding certification.

Surely, We provide the certification sample queries and references.

The certification has multiple impacts and encouraging factors in the recruitment of experts. Organizations claim that hiring certified professionals has served them with greater deployment and yield on investment. Check Cloud Career Building Up Force for New-age IT Professionals to understand the career benefits.

Artificial Intelligence Salary & Career Growth

One of the leading careers in artificial intelligence is the job of the research scientist. … Research scientists are in high demand and command an annual median salary of $99,809. Like data scientists, research scientists often are expected to have an advanced master’s or doctoral degree in computer science.

  • The median salary for an AI professional in India is Rs 14.3 lakhs per annum.
  • The entry-level salary is about Rs 6 lakhs per annum. Mid and senior-level executives could earn more than Rs 50 lakh per annum.However, this is across qualifications, skills, and experience.

The scope of Artificial Intelligence Training certified professionals are in the fields such as developing speech expert systems, game playing, robotics, recognizing machines, language detection machine, computer vision, and more. This industry has just started to increase and the scope for professionals certified in this Technology is very bright.

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Artificial Intelligence Job Support

We Provide job support by experienced and real-time working professionals. We can charge as per your convenience hourly or monthly. Feel free to ask initial interaction session with expert.