Artificial Intelligence Online Training

Artificial Intelligence is such a happening concept these days. And people are showing extreme eagerness to take a course in AI. This intelligence shown by machines is something that has a wide application and has been able to carve a niche for itself.

• Training by Realtime Expert trainer
• Live Online Classes
• Free study material
• Online virtual Classes available in morning, evening and weekend

To apply for the Artificial Intelligence Training in Hyderabad, you need to either:

• You need to have a good foundation in mathematical concepts like linear algebra, calculus, probability and statistics
• You need to know at least one programming language like Python or R.
• You need to have a good understanding of OOPs concepts, algorithms and data structures.
• You need to have some basic data analysis and data visualisation skills.

We guarantee learning at your convenience & pace.

• Instant Access:
  Get instant access to self-paced training after signup.
• Streaming video recording:
  Watch lessons any time at your schedule, free recording.
• Exercises:
  Practical exercises help you test what you are learning as you go.
• Free Demo:
  Sign up for free demo to check whether the course is right for you and interact with the faculty live.
• Experienced Trainers:
  We only hire the industry’s best trainers
• Live free interactive web sessions:
  Ask the Expert Shell Scripting trainers about the career prospects and clarify your questions any time after you complete the course.
• Structured Curriculum Schedule:
  Progress with your complete daily interactive lessons and assignments.
• Faculty Mentoring:
  Turn in daily and weekly homework for personalized feedback from faculty.
• Virtual Office Hours:
  Live interaction with the faculty and other students around the world.
• Hands on Live Projects:
  Work on live lab sessions to tackle real-world projects. Get 100% faculty guidance and ratings.

Artificial Intelligence Online Training

Module 1: Introduction to Data Science (Duration-1hr)

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to Python (Duration-1hr)

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

Module 3: Python Basics (Duration-5hrs)

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

Module 4: Python Packages (Duration-2hrs)

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

Module 5: Importing Data (Duration-1hr)

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to CSV file

Module 6: Manipulating Data (Duration-1hr)

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics (Duration-11hrs)

• Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
• Probability Basics
  • What does it mean by probability?
  • Types of Probability
  • ODDS Ratio?
• Standard Deviation
  • Data deviation & distribution
  • Variance
• Bias variance Tradeoff
  • Underfitting
  • Overfitting
• Distance metrics
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatment
  • What is NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • positive & Negative correlation

Module 8: Error Metrics (Duration-3hrs)

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  • MSE
  • RMSE
  • MAPE

Module 9: Machine Learning

Supervised Learning (Duration-6hrs)

• Linear Regression
  • Linear Equation
  • Slope
  • Intercept
  • R square value
• Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure Bias Variance Tradeoff
  • ROC curve
  • Bias Variance Tradeoff

Unsupervised Learning (Duration-4hrs)

• K-Means
• K-Means ++
• Hierarchical Clustering

SVM (Duration-2hrs)

• Support Vectors
• Hyperplanes
• 2-D Case
• Linear Hyperplane

SVM Kernal (Duration-2hrs)

• Linear
• Radial
• polynomial

Other Machine Learning algorithms (Duration-10hrs)

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

Module 10: ARTIFICIAL INTELLIGENCE

AI Introduction (Duration-9hrs)

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AL Applications

Module 11:  Deep Learning Algorithms (Duration-10hrs)

• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network
• ANN – Artificial Neural Network

Introduction to NLP (Duration-5hrs)

• Text Pre-processing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization

Text to Features (Feature Engineering) (Duration-5hrs)

• Syntactical Parsing
• Dependency Grammar
• Part of Speech Tagging
• Entity Parsing
• Named Entity Recognition
• Topic Modelling
• N-Grams
• TF – IDF
• Frequency / Density Features
• Word Embedding’s

Tasks of NLP (Duration-2hrs)

• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching