Artificial Intelligence Training by Experts
Our Training 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’s
Module 18: Tasks of NLP
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
- 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
- Web Development Courses in Mississauga
- Mobile App Develpment Courses in Mississauga
- Website Design Courses in Mississauga
- Networking and Security Courses in Mississauga
- Digital Marketing (SEO) Courses in Mississauga
- Graphics Training Courses in Mississauga
- Cloud Computing Courses in Mississauga
- Mean Stack Development Courses in Mississauga
- Python Full Packages Courses in Mississauga
- Animation & Video Editing Courses in Mississauga
- Mern Stack Development Courses in Mississauga
Course Highlights
Featured Courses
Computers, on the other hand, will avoid making these errors if they are correctly programmed. Firms can use this to simulate various scenarios and change the required activities to increase efficiency.
. Deep Learning is at the heart of AI, which might be a basic software development capability. The ultimate goal of AI is to create machines that can learn, plan, and solve problems on their own. 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. 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. Of course, simulating human intelligence is a difficult endeavor that necessitates expertise in areas such as cybersecurity, blockchain, robotics, generative AI with virtual reality, edge AI with IoT, and edge computing. Now and again, humans make blunders. Learn how to create efficient AI, self-sustaining systems, cutting-edge AI models, Deep Q-Learning, A3C, and much more.
