Artificial Intelligence Online Training

The Artificial Intelligence Training at Course Dx 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 Course Dx 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. Course Dx 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 Course Dx will help you to get your training easily with the latest skills and will make you certified and skilful in the AI platform.

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  • High-quality content created by industry experts
  • Lifetime access to high-quality self-paced learning and live online class recordings
  • Flexible, affordable options
  • Get complete certification guidance
  • 24×7 assistance and support
  • Attend a AI Online Course free demo before signing up


  • Self-Paced / Live Online training options
  • Flexible, affordable options
  • Learn as per full day schedule and/or flexible timings
  • Customize your own course content based on your project requirements
  • Get complete certification guidance
  • 24×7 assistance and support

AI Online Training Upcoming Batches


20,SEP 2020
Time: 7:00PM IST


20,SEP 2020
Time: 7:00PM IST


20,SEP 2020
Time: 7:00PM IST

Don’t find suitable time ?

₹ 37500


  • 65 hours high-quality video
  • 2 projects
  • 20 downloadable resource
  • Lifetime access and 24×7 support
  • Access on your computer or mobile

Introduction to Data Science
  • 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

Introduction to Statistics

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

Understanding And Implementation

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 Programming

Python Introduction

  • Python background, features
  • Installation and Various Python IDEs
  • Python vs Other languages


  • 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


  • 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


  • 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


  • 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


  • Introduction to Kafka
  • Kafka Producer
  • Kafka Consumer
  • Internals
  • Cluster membership and controller
  • Replication
  • Request processing
  • Data Storage
  • File processing
  • Compaction
  • Broker
  • Cluster architecture
  • Monitoring


  • Overview
  • Workflow
  • Scheduling in Oozie
  • Configuration files
  • Monitoring and Coordinator
  • Time and Data triggers
  • Oozie console


  • 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


  • 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

Yes, you can schedule your AI Certification Training in all Time Zones and we also offer training sessions with the US, UK, Australia, & Europe based trainers on Weekends and Weekdays.
Our expert trainer will provide the server access to the AI Online Course aspirants. Moreover, you will get practical training on Artificial Intelligence with live sessions that cover all your training needs about the course along with the Project.
Even if you have missed a class for the AI Training, then you can watch it as a recorded video within your LMS or attend the same in another live batch.
Our AI online Course trainer is a certified consultant and at present, he is working on a project within this technology and he is experienced too.
Yes, we will help you in getting certified in the Artificial Intelligence Course and we promise you that after completing our online training program, you will get a certificate in the technology for sure.
Our expert AI Online Course trainer will explain to you the subject and project work in detail on the software itself with live training. Each training batch of candidates is a software team within our Artificial Intelligence online training program and project work is offered to them. After the completion of the project work in the AI field, the training will be complete for the students. So, the trainees can feel the real-time IT company environment during our AI training program, where our trainer is a Team Leader.

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Lectures: 5