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Artificial Intelligence Online Course

Artificial Intelligence is the ability of a computer program that simulates human intelligence processes including NLP, expert systems, etc. Learn Artificial Intelligence in real-time with ITGuru experts practically with live training.

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Course Overview

The Artificial Intelligence Training at IT Guru will provide you the best knowledge on AI basics, intelligent machines, data science basics & importance, etc with live experts. Learning Artificial Intelligence Course makes you a master in this subject that includes statistics, mathematics, exploratory data analysis, machine learning techniques, etc. Our best Artificial Intelligence Course module will provide you a way to become certified in Artificial Intelligence. So, join hands with ITGuru for accepting new challenges and make the best solutions through the AI Certification Training. The AI Online Course basics and other features will make you an expert in the AI techniques, tools, framework, automation, to deal with real-time tasks. IT Guru provides the best Artificial Intelligence Course, where you will come to know how Artificial Intelligence is useful in different fields. The Artificial Intelligence Certification Training with IT Guru will help you to get your training easily with the latest skills and will make you certified and skillful in the AI platform.

Artificial Intelligence Online Course Objectives

  • Who can take Artificial Intelligence Training?

    There is no restriction to learn the AI course. People with an interest in automation and doing innovative things can take up this course.

  • What are the prerequisites for AI Certification Training?

    There are no prerequisites to join the Artificial Intelligence course. Basic knowledge of algorithms, functions & analyzing skills is good enough. But it’s not mandatory and our trainer will give you the initial idea of the course.

  • Why should you learn AI Online Course?

    The AI Online Course will help you to get practical knowledge of Artificial Intelligence, tools, automation, etc with real-time examples to get expert knowledge.

  • What do you learn in the best Artificial Intelligence Course?

    The best Artificial Intelligence course from IT Guru will give you real-time industry experience from AI experts on its usage, other technological aspects, etc.

  • What are the benefits of the Artificial Intelligence Course?

    After getting certified in our AI Training, you can expect yourself to work in a better position within a company with a better pay scale.

Artificial Intelligence Online Course Key Features

  • Lifetime Access

    You get lifetime access to the Learning Management System (LMS) where presentations, assignments, and installation guide on Artificial Intelligence Certification Training.

  • Assignments

    Trainers will assign some assignments soon after the completion of each and every topic that makes you master in the Artificial Intelligence Online Course and also helps you to clear Artificial Intelligence Certification.

  • Real-life Case Studies

    ITGuru trainers teach you each and every topic with real-world case studies that makes the learner understand in a better way

  • 24 x 7 Support

    We have 24x7 online support team to resolve all your queries

  • Certification

    IT Guru team has designed the Artificial Intelligence Online Course in the way to clear Certification as per the latest syllabus to make your dream come true.

  • Job Assistance

    IT Guru supports learners in finding job opportunities with the newly acquired skill set. Online IT Guru has a varied bunch of Clientele around the globe, over 200+ companies in various countries like the USA and India. Soon after the completion of the course, the support team will pass your resumes to the companies and ensure that the learners will achieve 100% placements.

Artificial Intelligence Online Course Syllabus

    • What is data Science? – Introduction
    • Importance of Data Science
    • Demand for Data Science Professional
    • Life cycle of data science
    • Tools and Technologies used in data Science
    • Business Intelligence vs Data Science vs Data Engineer
    • Role of a data scientist
    • Fundamentals of Math and Probability
    • Basic understanding of linear algebra, Matrices, vectors
    • Basics of Calculus
    • Various types and functions of matrices
    • Eigen vectors and Eigenvalues of a Matrix
    • Fundamentals of Probability
    • Types of events in Probability
    • Permutations & Combinations
    • Associative, Commutative and Distributive Laws
    • Descriptive Statistics
    • Describe or summaries a set of data Measure of central tendency and measure of dispersion.
    • The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness.
    • Histograms, Bar chart, Box plot
    • Inferential Statistics
    • What is inferential statistics Different types of Sampling techniques
    • Random variable
    • Probability Distribution and Cumulative Probability Distribution
    • Binomial Distribution & Quincunx
    • Normal Distribution & Normal variable
    • Sample Vs Population summary metrics
    • Point estimate and Interval estimate
    • Creating confidence interval for population parameter using Z* score and confidence level percentage
    • Bias & Variance trade-offs
    • Hypothesis Testing
    • Hypothesis Testing Basics
    • Null Hypothesis
    • Alternate Hypothesis
    • p-Value
    • False Positive & False Negative
    • Types of errors-Type 1 Errors, Type 2 Errors P value method, Z score Method
    • T-Test, Analysis of variance(ANOVA)
    • Exploratory Data Analysis
    • Introduction to EDA
    • Data Sourcing & Data cleaning
    • Fixing rows, columns
    • Missing values treatment and invalid values
    • Standardize values and filter data
    • Outliers treatment
    • Types of variables
    • Univariate Analysis on Unordered, ordered and quantitative variables
    • Rank-Frequency and Power Law distribution
    • Bivariate Analysis
    • Correlation
    • Various types of Derived metrics
    • Introduction to Machine Learning
    • What is Machine Learning?
    • Introduction to Supervised Learning, Unsupervised Learning & Semi-supervised Learning
    • What is Reinforcement Learning?
    • Variable Identification
    • CRISP-DM framework
    • Linear Regression
    • Introduction to Linear Regression and simple linear regression
    • Cost function, R-Square, RMSE and best fit line
    • Closed form and Gradient descent
    • Linear Regression with Multiple Variables
    • Disadvantage of Linear Models Interpretation of Model Outputs Understanding
    • Multi-collinearity
    • Adjusted R-Square, P-Value and VIF
    • Missing values & Outlier treatment
    • Understanding Heteroscedasticity
    • Signature of overfitting
    • Case Study
    • Application of Linear Regression for CTG data
    • Logistic Regression
    • Introduction to Logistic Regression
    • Binary Logistic Regression
    • Sigmoid function & Log of odds
    • Threshold Value
    • Multinomial Logistic Regression
    • Introduce the notion of classification Cost function for logistic regression
    • Application of logistic regression to multi-class classification.
    • Confusion Matrix, ROC Curve
    • AIC & BIC
    • Advantages and Disadvantages of Logistic Regression
    • Decision Trees & Random Forest
    • Decision Tree – C4.5, CART
    • How to build decision tree? Understanding CART Model Classification Rules
    • Overfitting Problem Stopping Criteria And Pruning
    • Under fitting
    • Gini Index
    • Entropy & Information Gain
    • MDS
    • How to find final size of Trees? Model A decision Tree.
    • Introduction to Random Forests
    • Ensembles & Bagging technique
    • Out of Bag error
    • Advantages of Random Forest over Decision Trees
    • Support Vector Machines
    • Introduction to SVM
    • Hyperplane & Linear discriminator
    • Maximal Marginal Hyperplane & Support vectors
    • Support Vector classifier
    • Slack variable
    • Boundary & Feature transformation
    • Kernel Trick
    • Handling non-linearity in the dataset using various Kernels
    • Case Study
    • Business Case Study with Cardio to co-graphic data
    • Unsupervised Learning
    • Feature Selection & Feature Extraction
    • Feature Construction
    • Hierarchical Clustering
    • K-Means algorithm for clustering – groupings of unlabeled data points.
    • Principal Component Analysis(PCA)
    • Association Rules
    • Case Study
    • Market Basket Analysis
    • Dimensionality reduction on CTG
    • Python Introduction
    • Python background, features
    • Installation and Various Python IDEs
    • Python vs Other languages
    • Basics
    • Operators in Python – Arithmetic, Relational, Logical and Assignment Operators
    • Variables, Types Of Variables
    • Naming conventions
    • String operations
    • Data Structures
    • Lists
    • Tuples
    • Sets
    • Dictionaries
    • Comprehensions
    • Python for Data Science
    • Numerical Python
    • ND array
    • Subset, slicing
    • Indexing
    • List vs ND array
    • Manipulating arrays
    • Mathematical operations and apply functions
    • Linear algebra operations
    • Pandas
    • Data loading
    • Series and Data frame
    • Selecting rows and columns
    • Position and label-based indexing
    • Slicing and dicing
    • Merging and concatenating
    • Grouping and summarizing
    • Lambda functions and pivot tables
    • Data Processing, cleaning
    • Missing Values
    • Outliers
    • Data visualization
    • Introduction to Matplotlib
    • Basic plotting
    • Figures and sub plotting
    • Box plot, Histograms, Scatter plots, image loading
    • Introduction to Sea born
    • Histogram, rugged plot, hex plot and density plot
    • Joint plot, pair plot, count plot, Heat maps
    • Plotting categorical data and aggregation of values
    • Plotting Time-Series data using tsplot
    • Understanding Big Data and Hadoop
    • What is Data?
    • Different types of Data
    • What is Big Data and the purpose? Where dowe use it?
    • Various Big Data technologies Why Hadoop?
    • Hadoop Eco system Rack awareness
    • HDFS Architecture
    • Hadoop 1.xvs 2.x
    • HDFS Cluster architecture
    • Resource management and configuration files
    • Slaves and master
    • Data loading techniques
    • Map Reduce
    • MapReduce Paradigm
    • Advantages of MapReduce
    • Architecture and various components of Map Reduce
    • YARN and workflow
    • Data orchestration in Map Reduce job flow
    • Combiners, Practitioners
    • Advanced Map Reduce
    • Joins
    • Various Data Types
    • Input formats
    • Output formats
    • MRUnit testing framework
    • Counters
    • Distributed Cache
    • Sequence File
    • Pig
    • What is Pig and why is it required?
    • Pig vs MapReduce
    • Pig components and structure
    • Data Types
    • Data structures
    • Pig limitations
    • Pig Latin
    • Operators
    • Functions
    • Hive
    • What is Hive and where to use
    • Pig Vs Hive
    • Hive architecture and components
    • Data Types and data models
    • Partitions, buckets
    • Data loading
    • Hive QL
    • UDF
    • Index
    • Views
    • Joins
    • Partitioning
    • H Base    
    • Introduction to No SQL Database
    • H Base storage architecture
    • H Base components
    • Regions
    • Client
    • Modes
    • Ports and utilities
    • Attributes
    • Data Model
    • Data Loading
    • H Base API
    • Zookeeper and H Base
    • Sqoop and Flume
    • Understand Data Ingestion
    • Introduction to Sqoop and Flume
    • Sqoop: Import from RDBMS to HDFS
    • Sqoop: Import from RDBMS to Hive
    • Sqoop Jobs
    • Flume architecture
    • Flume agent
    • Flume sinks
    • Flume channels
    • Executing the commands
    • Flume multi agent
    • Kafka
    • Introduction to Kafka
    • Kafka Producer
    • Kafka Consumer
    • Internals
    • Cluster membership and controller
    • Replication
    • Request processing
    • Data Storage
    • File processing
    • Compaction
    • Broker
    • Cluster architecture
    • Monitoring
    • Oozie
    • Overview
    • Workflow
    • Scheduling in Oozie
    • Configuration files
    • Monitoring and Coordinator
    • Time and Data triggers
    • Oozie console
    • Spark
    • Spark Overview and architecture
    • Spark Shell
    • Spark context
    • RDDs (Resilient Distributed Datasets) RDD
    • Operations
    • Partitioning
    • Transformations Actions
    • Key-value pair
    • Persistence
    • Spark streaming
    • Spark DStreams
    • Transformations
    • Request count
    • Spark SQL
    • Structured data processing
    • Spark with JSON & XML
    • Data frame operations
    • Working with CSV files & JDBC
    • Broadcast Variables
    • Accumulators
    • MapReduce vs Spark
    • Scala
    • Overview and background
    • Scala vs other languages
    • Environment setup
    • Scala compiler
    • Immutability
    • Variables and Various operators
    • Conditional statements and Loops
    • Lists, Tuples, Maps and options
    • Comprehensions
    • Functional programming in Scala
    • Object Oriented Programming
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    Self-Paced Learning

    16465 18500

    Get Free Trial
    This course includes
    • 60 hours high-quality video
    • 2 projects
    • 18 downloadable resource
    • Lifetime access and 24x7 support
    • Access on your computer or mobile
    • Get certificate on course completion
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    • Lifetime access to high-quality self-paced learning and live online class recordings
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