Artificial Intelligence Diploma Training

Artificial Intelligence is associated with a set of technologies impacting and guiding how users interact with and use internet. In the future this association of the computers and technology is continue to increase as more and more areas of human computer interaction are going to be impacted by AI. Quality Thought is at the forefront AI training. We have been working on this subject for some time now and our constant endeavor has ensured that we are able to offer the best artificial intelligence training program.

Quality Thought offers the best Artificial Intelligence Training program with best trainers and very comprehensive curriculum. Our Artificial Intelligence Training program focuses on constant upgrade of technology and programming in order to at par with the industry. Our programs on Artificial Intelligence are the most relevant and most advance programs offered by any training institutes. The primary
objective of the Artificial Intelligence training program is to help students and work professionals learn and master this latest technology which is of great demand in the market today.

Algebra and Calculus are integral to all learning involving Artificial Intelligence. Quality Thought understands the importance of this mathematical reference and provided an in-depth study on these mathematical concepts before advancing with other computing areas.

Python program is the key programming language used for working on various areas of AI. Our AI training program focus on Python programming, usage of 3rd party libraries and machine learning concepts are stressed.

Predictive Modeling, Machine learning algorithm, deep learning are some of the other key concepts and topics covered which helps students to understand and start working on AI related assignment and real- life project cases.

Big Data, Apche and other important data store and process management application are worked on to help students get a complete end-to-end application development and work on practical real-time cases on AI, helping you get market ready.

Artificial Intelligence program can be picked up by anyone with sound programming background, testing
and system admin experience. The course is applicable to:
Engineer Graduates
Working IT professional from programming, web development and DBA fields
Business Analyst
System Administrators

The recent acquisition of Deep Mind an AI startup by Google highlights the huge potential in the AI space. The technology is being taking seriously as more and more large established companies are investing heavily in the technologies associated with AI. The future is bright for the right professional who understands and is properly skilled in the AI space. At Quality Thought we understand the importance of proper learning and understanding of AI. This is what has motivated us to design and deliver the best Artificial Intelligence training program in the market.

AI and its associated programming are for people with sound engineering background. You need to have good programming skills and have sound understanding of Data management and processing and analysis. Prior experience as DBA, exposure to web development programming and other core programming is an added advantage.

• Data, Data Types
• Meaning of variables
• Central Tendency
• Measures of Dispersion
• Measures of Variability
• Measures of Shape
• Data Distribution
• Correlation, Covariance
• Practical Examples

• Mean, Expected value
• Binomial Random Variable
• Normal Distribution
• Poisson Random Variable
• Continuous Random Variable
• Discrete Random Variable
• Practical Examples

• Central Limit Theorem
• Sampling Distributions for Sample Proportion, p-hat
• Sampling Distributions for Sample Mean, x-bar
• Z- Scores
• Practical Examples

• Type I and Type II Errors
• Decision Making
• Power
• Testing for mean, variance, proportion
• Practical Examples

• Contingency Tables
• Independent and Dependent
• Pearson’s Chi-Square Test
• Misuses of Chi-Squared Test
• Measures of Association
• Practical Examples

• Analysis of Variance & Co-Variance
• ANOVA Assumptions & Comparisons
• F-Tests
• Practical Examples

• Installation of R & R Studio
• Getting started with R
• Basic and Advanced Data types in R
• Variable operators in R
• Working with R data frames
• Reading and writing data files to R
• R functions and loops
• Special utility functions
• Merging and sorting data
• Practice assignment

• Summarizing data, measures of central tendency
• Measures of data variability & distributions
• Using R language to summarize data
• Practice assignment

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs ( Bar/pie/line chart/histogram/boxplot/scatter/density etc)
• R Packages for Exploratory Data Analysis (dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
• R Packages for Graphical Analysis (base, ggplot, lattice,etc)

• Introducing statistical inference
• Estimators and confidence intervals
• Central Limit theorem
• Parametric and non-parametric statistical tests
• Analysis of variance (ANOVA)

• Needs & methods of data preparation
• Handling missing values
• Outlier treatment
• Transforming variables
• Data processing with dplyr package

• What is Python Programming?

• Installing Python
• Choosing an editor or IDE

• Building Hello World
• Variables and expressions
• Python functions
• Conditional structures
• Loops

• The date, time, and datetime classes
• Formatting time output
• Using timedelta objects
• Working with calendars

• Reading and writing files
• Working with OS path utilities
• Using file system shell methods

• Fetching internet data
• Working with JSON data
• Parsing and processing HTML
• Manipulating XML

• NumPy overview
• Creating NumPy arrays
• Doing math with arrays
• Indexing and slicing
• Records and dates

• Weather data overview
• Downloading and parsing data files
• Temperature analysis
• Integrating missing data
• Smoothing data
• Computing daily records
• Challenge
• Solution

• Pandas overview
• Series in Pandas
• DataFrames in Pandas
• Using multilevel indices
• Aggregation

• Baby name overview
• Loading datasets
• Name popularity
• A yearly top ten
• Challenge
• Solution

• Filter and select data
• Treat missing values
• Remove duplicates
• Concatenate and transform data
• Group and aggregate data
• Chapter Quiz

• Create standard line, bar, and pie plots
• Define plot elements
• Format plots
• Create labels and annotations
• Create visualizations from time series data
• Construct histograms, box plots, and scatter plots

• An Approach to Prediction
• Least Squares and Nearest Neighbors
• Statistical Decision
• Regression Models

• The Gauss–Markov Theorem
• Multiple Regression
• Forward- and Backward-Stepwise Selection
• Ridge Regression
• Lasso Regression
• Example using Python

• Linear Regression of an Indicator Matrix
• Linear Discriminant Analysis
• Logistic Regression
• Rosenblatt’s Perceptron Learning Algorithm
• Example using Python

• One-Dimensional Kernel Smoothers
• Local Linear Regression
• Local Polynomial Regression
• Mixture Models for Density Estimation and Classification
• Example using Python

• Bias, Variance and Model Complexity
• Optimism of the Training Error Rate
• Vapnik–Chervonenkis Dimension
• Cross-Validation

• Bootstrap and Maximum Likelihood Methods
• Relationship Between the Bootstrap and Bayesian Inference
• The EM Algorithm
• Bagging
• Example using Python

• Regression Trees
• Classification Trees
• Bump Hunting
• MARS: Multivariate Adaptive Regression Splines
• Example using Python

• Steepest Descent
• Gradient Boosting
• Regularization
• Interpretation
• Example using Python

• Fitting Neural Networks
• Over fitting
• Hidden Units
• Multiple Minima
• Single, Multi-Layer Perceptron
• Example using Python

• Support Vector Classifier
• Generalizing Linear Discriminant Analysis
• Flexible Discriminant Analysis
• Penalized Discriminant Analysis
• Example using Python

• Prototype Methods
• K-means Clustering
• Vector Quantization
• Gaussian Mixtures
• k-nearest Neighbors
• Example using Python

• The Apriori Algorithm
• Unsupervised as Supervised Learning
• Generalized Association Rules
• K-means Cluster Analysis
• Hierarchical Clustering
• Principal Components, Curves and Surfaces
• Non-Linear Dimension Reduction
• Example using Python

• Variable Importance
• Random Forests and Over fitting
• Bias
• Adaptive Nearest Neighbors
• Example using Python

• Introduction to Tensor Flow
• Perceptrons
• Artificial Neural Networks
• Gradient Descent
• Back Propagation
• Convolutional Neural Networks
• Recurrent Neural Networks
• Case Study

• Introduction
• Recognizing Natural Language Processing Applications
• Understanding NLP Tasks
• Tokenizing Text
• Removing Stopwords
• Identifying Bigrams
• Stemming and POS Tagging
• Disambiguating Word Meanings
• Contrasting Rule Based and Machine Learning Approaches
• Understanding Types of Machine Learning Problems in NLP
• Auto-summarizing Text
• Auto-summarizing Text Using a Rule-based Model
• Downloading an Article
• Preprocessing Article Text
• Extracting a Summary
• Classifying Text Using Machine Learning
• Outlining the Objective
• Building a Corpus of Tech Articles

• Introduction to Computer vision
• What is Open CV?
• Installation of OpenCV
• Download Python
• Install Python

• Working with images
• Forming images
• Storing images in Computer
• Gray scaling
• Color Spaces
• Representation of image
• Practical approach of creating images

• Transformations
• Image Translations
• Rotations
• Scaling, re-sizing & Interpolations
• Image Pyramids
• Cropping
• Brightening
• Darkening
• Image Masking
• Blurring
• Sharpening
• Dilation, Erosion
• Edge Detection
• Example

• Segmentation and contours
• Sorting contours
• Matching contour shapes
• Line detection
• Circle Detection
• Blob Detection
• Example

• Introduction
• finding specific pattern in an Image
• Feature description
• Finding corners
• SIFT
• SURF
• FAST
• BRIEF
• Detect a specific object using webcam
  • Face Detection
  • Eye Detection
  • Human Detection
  • Car Detection
  • Pedestrian detection

• Importance of Data
• Why Visual Analysis ?
• Why Tableau ?
• Tableau Extensions
• Understanding Navigation

• Tableau Desktop
• Tableau Prep
• Tableau Students Edition
• Tableau Server
• Tableau Public
• Tableau Reader
• Tableau Online

• Dimensions
• Measures
• Shelves
• Pills
• Show me
• Data Pane
• Groups
• Sets
• Dashboard
• Worksheet
• Stories

• Types of Data Connections
• Live Connection
• Extract Connection
• What is Extract File

• Data Types
• Data Values
• What is Data Source ?
• Connecting to DataSource
• Joins in Tableau
• When to use Joins
• Data Blending
• When to use Data Blending
• Joins vs Data Blending
• Custom SQL in Tableau
• Data refresh
• Filtering
• Sorting
• Hierarchies
• Drill down & Roll ups
• Grouping
• Creating Sets
• Working with Sets
• Parameters
• Creating Parameter
• Parameter Controls
• Aggregation

• Charting
• Line Graphs
• Blended Axis
• Dual vs Blended axis
• Horizontal Bar chart
• Vertical Bar Chart
• Stacked Bar Chart
• Pie Charts
• Gantt Charts
• Mapping
• Heat Maps
• Filed Maps
• Geo-Coding
• Formatting
• Advanced Charting
• Water Fall Charts
• Donut Charts
• Funnel Charts
• Lollipops Charts
• Whisker plots
• Scatter plots

• String Calculations
• Date Calculations
• Boolean Calculations
• Functions

• Linear Model
• Logarithmic Model
• Exponential Model
• Polynomial Mode

• What is Dashboard ?
• Basic Dashboarding
• Advanced Dashboarding
• Formatting
• Actions
• Creating a Story

• Tableau Reader
• Tableau Public
• Tableau Server
• Tableau Online

Students can benefit from our State of the art lab infrastructure facilities at all our training centers across the city. Our lab facilities are available through the day on all working days. For our online students, they can connect to our servers and other lab facilities over the internet and practice. These facilities are available 24X7.

If the student misses out of attending any session, he or she can re-attend the session by:
1. Attending the same session in another batch if student is attending classroom based session.
2. For online sessions, recording of the classes can be accessed by the student at all time to help revisit and listen the sessions missed out.

All our trainer are real-time industry experts with minimum of 10+ years of experience. Complete profiles of our trainers are available for review at our center and students are free to come interact with them and know more about them, before enrolling for programs.

All training programs conducted by Quality Thought are available in 3 modes, instructor based classroom programs, instructor based live online training and self-paced video based training. Students can choose

All discounts are subject to case-to-case basis. Please feel free to meet our administration staff to have a better discussion on the same. We do offer a variety of discounts and concession to our students coming in from different backgrounds.

For all corporate training requirements please feel free to get in touch with our administration staff managing corporate marketing and interaction. We have of the finest programs and offer to corporate with best-in-class programs.