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Artificial Intelligence Online Training

This Artificial Intelligence course and certification program has been crafted by our leading team with Artificial intelligence and its related concepts. Artificial intelligence is a concept that is used to build smart machines. These machines would show human intelligence while performing tasks. Today many companies are adopting Artificial intelligence tools to minimize the human effort and intelligence to achieve accuracy and speed. This Artificial intelligence program will make you aware of all the concepts of AI and how to use Artificial intelligence in different fields. This Artificial intelligence training program will make you proficient and confident in building AI applications.

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Artificial Intelligence Course Overview

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Artificial Intelligence is a branch of computer science that deals with building machines or computers that use their intelligence and perform a task. This reduces the human effort and errors that may occur. Today many industries are adopting AI to improve their efficiency and reduce human effort. There are different types of Artificial intelligence that are being used and this training program will give in-depth knowledge of all these types. Artificial intelligence's future is very advanced. They would be like machine learning systems that are able to interact with humans and understand the requirements. This program will enrich with the best and latest advancement in the field of AI.

Learn AI Key Features

  • Get complete idea about Introduction to Artificial Intelligence
  • Know what are the key concepts along with deep learning techniques
  • How to learn artificial intelligence material will also be provided
  • We will also train installation of artificial intelligence software
  • Be aware of benefits and incorporated programming languages
  • Learn about Python, Data sciences which will provide in-depth knowledge incordination with AI.

Who should take this course?

This course is suitable for all individuals who want to pursue a career in Artificial intelligence. This additionally benefits professionals in the field of programming and big data. And whoever is interested in the artificial intelligence certificate. We will provide you with a complete Artificial intelligence lesson plan.

Course curriculum
    • Overview of Data Science, AI, and ML
    • Use Cases in Business and Scope
    • Scienti?c Method
    • Modeling Concepts
    • CRISP-DM Method
    • Programming
      • Commands and Syntax of R
      • What are the different Packages and Libraries and how are they used in R
      • Introduction to Data Types
      • Explore Data Structures in R - Matrices, Vectors, Arrays, Factors, Data Frames, Lists
      • Methods Importing and Exporting Data.
      • How to use Control structures and Functions
    • Descriptive Statistics
      • Various types of Data exploration (bar chart, histograms, box plot, scatter plot, line graph,)
      • What is Qualitative and Quantitative Data
      • Measure of Central Tendency (Mean, Median and Mode),
      • Measure of Positions ( Quantiles, Quartiles, Deciles, and Percentiles)
      • Measure of Dispersion ( Median, Quartiles, Variance, Standard deviation, and Absolute deviation about median), Anscombe's quartet
      • Other Measures: Interquartile Range, Quartile and Percentile
    • Initial Data Analysis
      • Relationship between attributes: Chi Square, Covariance, Correlation Coef?cient,
      • Measure of Distribution (Kurtosis and Skewness ), Box and Whisker Plot (Box Plot and its parts, Using Box Plots to compare distribution) and other statistical graphs
    • Probability
      • Understanding Probability (Conditional probabilities, Joint, and marginal)
      • About Probability distributions (Continuous and Discrete)
      • How to use Density Functions and Cumulative functions
    • Gather information from different sources.
    • Explain internal systems and External systems.
    • What are Web APIs, Open Data Sources, Data APIs, Web Scrapping
    • Relational Database access (queries) to process/access data.
    • Concepts of Wrangling, Data Munging
    • What are Plyr packages and use of it?
    • What is Cast/Melt?
    • What is Data imputation?
    • Different types of Data Transformation ( log transform, z-score transform, Wrangling etc.,).
    • Standardization, Binning, and Classing
    • Anomalies and Outlier/Noise
    • What is Bag-of-words model and how to use it?
    • What are Regular Expressions
    • Tokenization and Sentence Splitting
    • Incorrect spellings, Punctuations and Stop words
    • Properties of words and Word cloud
    • Understanding Lemmatization and Term-Document TxD computation
    • Sentiment Analysis (Case Study)
    • Introduction to Big Data
    • Challenges of processing Big Data (Velocity, Volume, and Variety perspective)
    • Use Cases
    • Programming, Processing, and Storage Framework
    • Hadoop eco-system Components and their functions
    • How to use Essential Algorithms (Page Rank, Word count,  IT-IDF)
    • Spark: RDDs, Streaming and Spark ML
    • NoSQL concepts (CAP, ACID, NoSQL types)
    • Science of Visualization
    • Visualization Periodic Table
    • Aesthetics and Story telling
    • Concepts of measurement - scales of measurement
    • Design of data collection formats with illustration
    • Principles of Data Visualization – Various methods of presenting data in business analytics.
    • Concepts of Shape, Size, Color
    • Various Visualization types
    • How to display Bubble charts
    • What are Geo-maps (Chlorpeths)
    • What are Gauge charts and how to display them?
    • What is Tree map?
    • What is a Heat map
    • How to display Motion charts
    • What are Force Directed Charts etc.,
    • Sample versus population
    • Sample techniques (stratified, simple, random, clustered)
    • What are Sampling Distributions?
    • Parameter Estimation methods
    • Imbalanced data treatment
    • How to understand the data, attributes, distributions
    • Procedure for statistical testing, etc.
    • Test of Hypothesis (Concept of Hypothesis testing, Null Hypothesis and Alternative Hypothesis)
    • What is meant by Cross Tabulations and how to create them (Fisher’s exact test, Contingency table, their use, and Chi-Square test)
    • What are the conditions for One Sample t test (Performing the test, interpretation of results,  Concept, Assumptions, Hypothesis, Veri?cation)
    • Independent Samples t test
    • Paired Samples t test
    • One way ANOVA (Post hoc tests: Fisher's LSD, Tukey's HSD).
    • What is Z-test and F-test and what is the difference?
    • Regression basics: Relationship between attributes using Correlation and Covariance
    • Relationship between multiple variables: Regression (Multivariate, Linear) in prediction.
    • Residual Analysis
    • Identifying multi- collinearity, signi?cant features, feature reduction using AIC
    • Non-normality and Heteroscedasticity
    • Hypothesis testing of Regression Model
    • Con?dence intervals of Slope
    • R-square and goodness of ?t
    • In?uential Observations – Leverage
    • What is Polynomial Regression
    • Regularization methods
    • Concepts of Lasso, Ridge and Elastic nets
    • Categorical Variables in Regression
    • What is Logit function and interpretation?
    • Types of error measures (ROCR)
    • What is Logistic Regression in classi?cation?
    • Trend analysis
    • Seasonal and Cyclical analysis
    • Box-Jenkins, Moving averages, Holt-winters, Auto-correlation; ARIMA, Smoothing
    • Applications of Time Series in ?nancial markets
    • ML Techniques overview
    • Validation Techniques (Cross-Validations)
    • Dimensionality reduction/Feature Reduction
    • Principal components analysis (Eigen vectors, Eigen values, Orthogonality)
    • Concepts of Distance measurement
    • Different clustering methods (Hierarchical, Distance, Density)
    • What is Iterative distance-based clustering
    • Dealing with continuous, categorical values in K-Means
    • Constructing a hierarchical cluster
    • K-Medoids, k-Mode and density-based clustering
    • Measures of quality of clustering
    • Naïve Bayes Classi?er
      • Model Assumptions, Probability estimation
      • Required data processing
      • M-estimates, Feature selection: Mutual information
      • Classi?er
    • K-Nearest Neighbors
      • Computational geometry; Voronoi Diagrams; Delaunay Triangulations
      • K-Nearest Neighbor algorithm; Wilson editing and triangulations
      • Aspects to consider while designing K-Nearest Neighbor
    • Support Vector Machines
      • Linear learning machines and Making Kernels, Kernel space and working in feature space
      • SVM for classi?cation and regression problems.
    • Decision Trees
      • ID4, C4.5, CART
    • Ensembles methods
      • Boosting and Bagging and its impact on variance and bias
      • C5.0 boosting
      • Random forest
      • Gradient Boosting Machines and XGBoost
    • The applications of Association Rule Mining: Recommendation Engines, Market Basket, etc.
    • A mathematical model for Large item sets,  Association Rules, Association analysis
    • Apriori: Constructs large item sets with mini sup by iterations; Interestingness of discovered association rules;
    • Application examples; Association analysis vs. classi?cation
    • FP-trees  
    • AI: Application areas
    • AI Basics ( Greedy, Branch and Bound, Gradient Descent, Divide and Conquer)
    • NN basics (Back propagation, Perceptron and MLN, FFN)
    • Image classi?cation
    • Text classi?cation
    • Image classi?cation and hyper-parameter tuning
    • Emerging NN architectures
    • Building recurrent NN
    • Long Short-Term Memory
    • Time Series Forecasting
    • Auto-encoders and unsupervised learning
    • Stacked auto-encoders and semi-supervised learning
    • Regularization - Dropout and Batch normalization
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Artificial Intelligence Online Training FAQ’s:
1.What Is Artificial Intelligence Definition?

In simple terms Artificial Intelligence is a branch of computer science where it is used to build machines or robots which are capable of thinking like humans while performing tasks. This reduces human effort.

2.What are the types of Artificial Intelligence?

There are three types of Artificial Intelligence.

  • Artificial narrow intelligence
  • Artificial general intelligence
  • Artificial superintelligence

3.What is the AI tools list?

Some of the best Artificial intelligence tools are:

  • Microsoft Azure
  • Google Cloud
  • IBM Watson
  • Infosys Nia

4.How do I get Artificial intelligence certification?

We provide certification upon completing the course successfully. Our certification has standing in the market which will be an added advantage to you while hiring.

5.Artificial Intelligence Vs Machine Learning - Do they differ?

Artificial intelligence is a concept where it helps in building smart machines which can think and behave like humans while performing tasks.

Machine learning is an application of AI where the machines learn from the data provided by the AI.

6.Is Artificial intelligence difficult to learn?

Not really, it would be easier for individuals with a programming background. And others would need to spend time to grasp the concepts used in A. I The more you work on this the easier it will get.

7.What is the average artificial intelligence salary?

The average Artificial intelligence engineer salary is $114k per year.

8.What is artificial intelligence in medicine?

Usage of Artificial intelligence in medicine has increased more than ever. It is changing the face of medical innovations. A. I used to think, analyze and interpret the complex healthcare data where it was done by human effort.

9.Do you provide any materials on artificial intelligence?

Yes, We provide Artificial intelligence books, Journals like the international journal on artificial intelligence tools.

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Cutting-edge curriculum with job-ready training methodologies aligned with industry requirements.

Situational help and work assistance

Top-notch experts bring in the best practices and assignments, with live availability.

Learn & Practice on real-world problems.

Practice on real-world scenarios and data sets with hands-on experience.

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