Artificial Intelligence

Online/Offline Training Available

Join Webskitters Academy and learn Artificial Intelligence or AI Online Course with interactive virtual classes conducted by industry experts.

Minimum Eligibility:

Graduates/B.Tech/M.Tech/BCA/MCA/Diploma

Course Duration:

1 Month

Mode of Training:

Online/Offline

ARTIFICIAL INTELLIGENCE OR AI ONLINE COURSE

Objectives

Artificial Intelligence or AI course at Webskitters Academy is aimed to provide an introductory overview of the sector to the students. We have designed the course to help the candidates is to gain an in-depth information about the field. You will gain an exposure to the Artificial Intelligence technologies, understand the AI concepts, and various networks associated with the field. The course will help you to gain basic introduction to the Artificial Intelligence, and specialize in the sector.

Pre-Requisites

Basic Knowledge of Python.

Course Content

Module 1: Machine Learning (A talk with Machines)

Introduction
Understanding the theory and concepts of Machine Learning
Machine Learning Methods
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Top 8 programming Languages for Machine Learning & Data Science

Module 2: Python (A tour to Python Programming)

Installation of Anaconda
Tokens in Python
Keywords
Identifiers
Literals
Operators
Strings
String Methods
Strings Indexing and Slicing Loop
List, Tuple, Dictionary, Set Map, Function, Lambda
Class Programs and Assignments

Module 3: Matrix Manipulation using Numpy

NumPy Arrays
Indexing and Selection
Slicing
Numpy Operations
Class Programs and Assignments

Module 4: Data Analysis With Pandas

Make (Read & Write) CSV Dataset files
Series & Data Frames
Missing Data Group by Merging, Joining Concatenating
Class Programs and Assignments

Module 5: Data-Visualization with-Matplotlib&Seaborn

What is data visualization?
Importance of data visualization
Plotting, Plot, Sub-plot & Special Plot Distribution
Plots using Seaborn Categorical
Plots using Seaborn Matrix
Plots using Seaborn
Style and Colour using Seaborn
Class Programs and Assignments

Module 6: Data Processing Using Titanic Dataset

Reading Data
Handling Missing Data
Categorical Data
Imputation
Sampling
Transform data
Standard Scaler
Decomposition
Aggregation
Splitting Data in Training & Testing Set

Module 7: Basic Statistics and Probability

Mean, median, mode, variance
Bayes theorem
Joint probability distribution
Basics of Vectors
Class Programs and Assignments
Introduction to features selections
Univariate features selections
Recursive features selections
Class program of sklearn Features
Selection

Module 8: Different types of Learning

Difference between supervised learning, unsupervised learning, and reinforcement learning
Famous Algorithms for Data Science and Machine Learning
Understanding of these Algorithms

Module 9: Algorithm

Statistics of linear regression
Linear regression
Logistic-regression
k nearest neighbors
k mean algorithm for clustering
Statistics of k mean

Module 10: Decision Trees and Ensemble Learning

Concept of decision trees and random forests
Decision trees and random forest Bagging, boosting, voting classifier Implementation of bagging, boosting, voting classifier using scikit learn
Understanding of count vectorizer and Naive Bayes
Support Vector Machine(SVM)
SVM using scikit learn
bias variance trade off errors due to variance
Understanding the theory of Perceptron and Gradient Descent algorithm along with practical demonstration

Module 11: Deep Learning&Recommendation Engine

Neural Network
Convolutional Neural Network
Back Propagation
Building recommendation engine using python
Perceptron and Gradient Descent algorithm

Module 12: Image Processing with OpenCV

Template Matching
Corner Detection
Edge Detection
Feature Matching
Project: Face Detection in Image
Project: Face Detection in Real time

Module 13: NLP &ReinforcementLearning

Introduction to token and corpus(Practical with demo project)
Introduction to Reinforcement Learning
Understanding the concepts of Reinforcement Learning
Implementation of Reinforcement Learning

Module 14: Artificial Intelligence

PROJECTS

Students Get Placed In

What Our Recruiters are Saying

We hired the students from Webskitters Academy and to our surprise we found out that they are brilliant in their work! No way we can call them students, they are professionals. They know their work, have the skills and are well-groomed for the profession. Thanks, Webskitters Academy for such a comprehensive training!

Payal Chatterjee HR Manager

“We are happy with the quality of training that the Webskitters Academy students have received. Few of them have been on-board with us recently and they have impressed us. We would recommend our associates to hire freshers from this institute, they make sure that the students are well-trained and prepared for the industry.”

Sayani Dey HR Executive

“We are glad to have hired the students from Webskitters Academy! We have to mention that they are trained to be the professionals. From the first day onwards, they have shown their excellence, and it is very impressive. Our team is also happy with their involvement and performance. Looking forward to hire more excellent students from them!”

Rounak Dhar HR Executive

“It was for the first time that we hired students from Websitters Academy. We had some doubts regarding freshers, but to our surprise, they are very good in their work. They know exactly how to do a particular job keeping the industry standards in mind. Our organization is happy to have them. Their codes, and development skills are perfect, suitable for the industry. It proves that they have been trained comprehensively. Good work by the faculties of Webskitters Academy. We will definitely recruit more young talents from you.”

Soma Das Executive HR, Allide Media

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