Artificial Intelligence Online Live Class by Experts
Our Training/Internship Process
Artificial Intelligence - Syllabus, Fees & Duration
Module 1: Introduction to Data Science
- 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
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
Module 3: Python Basics
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Module 4: Python Packages
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Module 5: Importing Data
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 6: Manipulating Data
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 7: Statistics Basics
- Central Tendency
- Probability Basics
- Standard Deviation
- Bias variance Trade off
- Distance metrics
- Outlier analysis
- Missing Value treatment
- Correlation
Module 8: Error Metrics
- Classification
- Regression
Module 9: Machine Learning
- Supervised Learning
- Linear Regression
- Logistic regression
Module 10: Unsupervised Learning
- K-Means
- K-Means ++
- Hierarchical Clustering
Module 11: SVM
- Support Vectors
- Hyperplanes
- 2-D Case
- Linear Hyperplane
Module 12: SVM Kernel
- Linear
- Radial
- polynomial
Module 13: Other Machine Learning algorithms
- K Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree CART
- Decision Tree C50
- Random Forest
Module 14: ARTIFICIAL INTELLIGENCE
- Perceptron
- Multi-Layer perceptron
- Markov Decision Process
- Logical Agent & First Order Logic
- AL Applications
Module 15: Deep Learning Algorithms
- CNN Convolutional Neural Network
- RNN Recurrent Neural Network
- ANN Artificial Neural Network
Module 16: Introduction to NLP
- Text Pre-processing
- Noise Removal
- Lexicon Normalization
- Lemmatization
- Stemming
- Object Standardization
Module 17: Text to Features
- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Named Entity Recognition
- Topic Modelling
- N Grams
- TF IDF
- Frequency / Density Features
- Word Embedding
Module 18: Tasks of NLP
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 60 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsNEAR.in
- Training/Internship Certificate
- Agra
- Ahmedabad
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- Aurangabad
- Bangalore
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- Coimbatore
- Delhi
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- Gurgaon
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- Hyderabad
- Idukki
- Indore
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- Kanpur
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- Kerala
- Kolkata
- Kollam
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- Malappuram
- Mangaluru
- Mumbai
- Mysuru
- Nagpur
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- Navi Mumbai
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- Palakkad
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- Pune
- Punjab
- Salem
- Shimla
- Solapur
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- Thane
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- Tiruchirappalli
- Tiruppur
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- Varanasi
- Vellore
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- Visakhapatnam
- Wayanad
Course Highlights
Our Locations
Even though AI has been studied for more than half a century, we have yet to produce a machine that is as intelligent as a human.
AI approaches will support reducing these errors by automating some responsibilities or assisting workers in their work. Live Project Sessions for hands-on learning.
AI uses many algorithms to method knowledge, generate data-driven judgments, and complete tasks in a manner comparable to that of a person. Our online education program requires you to become an Advanced Industrial Technologies specialist. The creation of Ai may necessitate the use of a variety of languages and technologies to create a solution that meets a variety of aims and requirements. We do, however, have several successful applications. Data Analytics is the pre-processing activity of AI. Computers, on the other hand, will avoid making these errors if they are correctly programmed. Professors need to spend more time one-on-one with pupils, conducting research, and current their own education, but they don't have the time to do so. 
