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Best Machine Learning with Python or R Course in Bangalore & Top Machine Learning with Python or R Training Institute
ü Artificial
Intelligence Course Content In Bangalore
ü
Description
ü
AI is any technique, code or algorithm that
enables machines to develop, demonstrate and mimic human cognitive behavior or
intelligence and hence the name “Artificial Intelligenceâ€. Some of the most
successful applications of AI around us can be seen in Robotics, Computer
Vision,
ü
Virtual Reality, Speech Recognition, Automation,
Gaming and so on…
ü WHY
YOU SHOULD TAKE THIS TRAINING COURSE?
ü
Artificial Intelligence is constantly pushing
the boundaries of what machines are capable of. The Main purpose of training
real time smart machine is to use their speed and capability. Most importantly
machine can think and perform task like humans. By this course student will be
able to design and develop an advance AI System.
ü Learning
Outcomes
ü
Python Programming for ML
ü
Supervised based algo implementation
ü
Matplotlib for graph plots with linear
regression
ü
Live Image Processing
ü
Image Recognition
ü
NLP and Cloud Connectivity
ü
Secured AI with ML and IoT
ü
Introduction
ü
Artificial Intelligence
ü
Introduction to Artificial Intelligence (AI)
ü
History of AI
ü
Importance and other Philosophies about AI
ü
General Approaches and Goals of AI
ü
Components of AI
ü
Working Domains/Companies/Products in Current
Market
ü
Programming Languages Used for AI
ü
Python Programming
ü
Python Programming
ü
Basic of python and why python for machine
learning
ü
Installation of software on different OS.
ü
Understanding basic syntax with data types
ü
Number, String, List, Tuple, Dictionary
ü
Extracting data from a file
ü
Committing your code to GIT
ü
More About Python Programming
ü
Conditional statement and loops
ü
Function and modules
ü
File handling
ü
Creating own modules / library
ü
Web scraping with urllib2
ü
Grabbing system information from Popen and os
library
ü
Scanning Network IP & MAC address with loops
ü
Libraries Used
ü
Introduction to Numpy & Matplotlib
ü
Managing arrary with numpy
ü
Multidimensional array with numpy
ü
Unit matrix handling & creating
ü
Deleting indexes from matrix
ü
Deep dive with Matplotlib
ü
Drawing general purpose graphs
ü
Graphs with mathematics
ü
Machine Learning Techniques
ü
Types of learning
ü
Advice of applying machine learning
ü
Machine learning System Design
ü
Decision Tree Classifier
ü
Training your machine with real time datasets
ü
Deep dive with UCI
ü
Lab session for loading data from different APIs
ü
Detecting data from numpy and converting for
training and testing data
ü
testing data
ü
Exercise with ML and others framework
ü
Introduction to iris datasets
ü
Understanding iris datasets
ü
Modifying and loading with scikit-learn
ü
Separating data with numpy
ü
Training classifier
ü
Algo data process view
ü
Decision Tree understanding
ü
Linear Regression
ü
Using House Price Prediction
ü
Simple Linear Regression
ü
Polynomial Linear Regression
ü
Cost Function of Linear Regression
ü
Understanding linear regression using matrix
ü
Logistic Regression
ü
Using Iris dataset to understand logistic
regression
ü
Concept of linearly separable data
ü
Cost Function & Mathematical Foundation
ü
Using Iris dataset to understand logistic
regression
ü
Concept of linearly separable data
ü
Cost Function & Mathematical Foundation
ü
Neural Networks Analysis
ü
Introduction to Neural Network
ü
Understanding neural networks
ü
Data learning and machine predictions
ü
Neural networks real understanding
ü
Neural network implementation with real datasets
ü
Natural Language Processing
ü
Tokenizing text data
ü
Converting words to their base forms using
stemming
ü
Converting words to their base forms using
lemmatization
ü
Dividing text data into chunks
ü
Extracting the frequency of terms using a Bag of
Words model
ü
Building a category predictor
ü
Constructing a gender identifier
ü
Building a sentiment analyzer
ü
Topic modeling using Latent Dirichlet Allocation
ü
More About ANN
ü
Perception
ü
Back Propagation/Training Algo’s
ü
Convolutional & Recurrent and Artificial
Neural Networks
ü
Deep Neural Network
ü Natural
Language Processing (NLP)
ü
Introduction to NLP
ü
Word Representation Model
ü
Sentence Classification
ü
Language Modeling
ü
Project:- Building AI based ChatBot ussing
Tensorflow
ü
Feature Engineering
ü
Categorical Features
ü
Text Features
ü
Image Features
ü
Derived Features
ü
Imputation of Missing Data
ü
Feature Pipelines - Transformer & Estimator
ü
Naive Bayes Classification
ü
Bayesian Classification
ü
Gaussian Naive Bayes
ü
Multinomial Naive Bayes
ü
When to Use Naive Bayes
ü
Application : Identify category from text
ü
K-Means Clustering
ü
Introducing k-Means
ü
Understanding cost function for unsupervised
algorithms
ü
Elbow rule to decide number of clusters
ü
Application : Image compression
ü
Application : Detection of number of characters
in arabic
ü
Decision Trees And Random Forests
ü
Understaning Decision trees
ü
Printing tree
ü
Motivating Random Forests: Decision Trees
ü
Ensembles of Estimators: Random Forests
ü
Random Forest Regression
ü
Application: Random Forest for Classifying
Digits
ü
Other Boosting techniques - AdaBoost, Gradient
Tree Boosting
ü
No Interview Questions Found..
Course fees
Working Professionals as trainers
Trainers Experience
Student Web Portal
Class Room Infrastructure
Reference Pay
Instalment
Lab Infrastructure
Who are our trainers?
Student’s Ratings
Trust & Credibility
Fees Negotiable?
Very competitive and affordable.
Yes
Min 7+ Years experience
We have a dedicated students portal
All classrooms are Ventilated with power backups.
We pay Rs 1000 for every student you refer.
Yes its very flexible, you can pay the fees in instalment
We Have Excellent Lab Facility and Provide server access
IT consultants,Solutions Architects, Technical Leads
5 ***** ratings from more than 4000 students
Very High
Definitely yes we understand the financial situation of each student
Really less fees but compromise with the quality
Very Few
Trainers have less exposure to real time work.
None
Very few institutes
None
Very few institutes
None
hire full time trainers with very little experience
Mixed
Moderate.
Very few