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Artificial Intelligence course in Bangalore

Sniffer Search provides Best Artificial intelligence and Machine Learning course in bangalore .
Our instructor are industry expert capable of building product using Artificial Intelligence

The Artificial Intelligence course focus on of machine learning with 23 sample Data set using Python package and libraries (numpy,scipy,pandas,scikit-learn and matplotlib).

Our deep learning and reinforcement learning part is best in artificial Intelligence training and we provide individual attention and train every candidate according to her/his adaptability.

7 STUDENTS ENROLLED

 

At the end of  the course of  Artificial Intelligence ,  you will learn:

  • Implement machine learning  of your own
  • Understand Neural network  and Deep learning  with Reinforcement learning algorithm which are used for Artificial Intelligence product
  • Deep Learning used in Image classification,NLP,automated car (latest Artificial Intelligence algorithm)
  • Natural language processing  and speech to Text to build  Artificial Intelligence product
  • Learn how to optimize machine learning algorithm to increase  accuracy
  • Understand Dimension reduction and apply to reduce feature to increase performance

Why Learn from us:

Sniffer Search is the market leader in Artificial Intelligence and Machine learning courses and training institute in Bangalore.

we provide Artificial Intelligence and Deep Learning  training  in Bangalore and  other Major Indian Cities like Delhi,Mumbai,Chennai,Hyderabad,Pune  .

We have Special training on Artificial Intelligence for location U.S(New York ,California and other major cities) online via instructor .

This is a job oriented course  on Artificial Intelligence and machine learning  which will qualify you for Deep Learning course from Sniffer Search where you will also learn topic like Self driving car.

This world will replace all low IQ job with Artificial Intelligence . So there are requirement of million of techie who can  contribute to artificial Intelligence  to make our daily life easy .

Artificial Intelligence in real life today can be seen everywhere.For example:

  • Artificial Intelligence can help  identify critical illness
  • Autonomous vehicle using artificial Intelligence uses neural network and reinforcement algorithm
  •  Robot to help human like cops,industry,house cleaning etc using artificial intelligence.
  • Drone as camera and Delivery Service
  • Intelligent retrieval of knowledge base
  • Expert advice System

Exposure to Tools and Technologies in combined course of Artificial Intelligence and Deep learning are:

PredictionIO,Deeplearning4jTorch,AI-One ,Protege , DiffBlue , Google’s TensorFlow ,Nervana Neon,Veles ,Apache Mahout

Other Assistance to help you learn smoothly this course are

  • 40 Hours of Live Online Training
  • 24X7 eLearning Access, and support over Email
  • Mentorship from Industry Experts
  • Instant Resolution of Doubts and Queries
  • Dedicated Support and Guidance
  • Course Completion Certificate from

Job Assurance:

  • After completing the course you will be able to get the best of data science job as we take care of learning from concept to practical  with real time use case of Artificial intelligence .
  • Data Scientist skills has shortage of 2 million in India itself
  • Data Scientist are high paid professional among all technology
  • We provide list of companies hiring on data science ,machine learning and Artificial Intelligence and  we forward and schedule  all competent candidate resume to prospect employer after completion of training

 

Course Curriculum

Overview of Data Science:
Overview of Artificial Intelligence
Artificial intelligence evolution 00:00:00
The scope of Artificial Intelligence 00:00:00
Artificial Intelligence: in Healthcare 00:00:00
Data Wrangling and data Exploration
Data Wrangling and Data Exploration Handle issues in Data Wrangling Model selection in Data Exploration 00:00:00
How to approach the data Focus on data Assumption EDA Technique (Quantitative and Graphical) 00:00:00
Introduction of Statistics
Inferential method Descriptive Method Discussion on Mean ,Median,Variance and Standard Deviation 00:00:00
Statistical Analysis
Range ,Frequency and Central Tendency Measures of central tendency Histogram (Graphical Representation) Bell Curve and Kurtosis explain with Graph Statistical Technique(Hypothesis Testing) Hypothesis Testing-Process and Steps Error type in hypothesis testing Perform Hypothesis testing on different data type Chi-Square Test 00:00:00
Installation of anaconda Installation of Jupyter notebook Python Variables Python Strings,loops,conditions Programming construct of Python 00:00:00
Python Installation and Environment setup
Python data Structure for Data Science implementation
List with example Python method ,class and object Tuples,element access in tuples Example of tuples Slicing tuples Dictionary(access and modify Dictionary elements with example) Set with example 00:00:00
: Python Package for Scientific computing - Numpy
Class and basic operation of ndarray Accessing array elements Copy and view Numpy method for shape manipulation Linear Algebraic function 00:00:00
Python library Scipy
Scientific calculation with Scipy Scipy sub package-optimization Python package Pandas Example of how to create a series with pandas Vectorized operations in Series Create Data Frame from dictionary Handle missing values with example 00:00:00
Introduction to Machine Learning- supervised and unsupervised learning
Linear regression with single and multiple variable to solve predictive problem of machine learning
Gradient Descent of Machine Learning
Logistic regression -a Machine learning approach to solve binary problem -Discussion of Titanic Data Set
Support Vector Machines in Machine Learning -using kernel functions to separate complex data with 1000 of featues
Non Linear hypothesis in Machine Learning
Cost Function Evaluating model in machine Learning
Unsupervised Learning -K means Algorithm in machine learning
Anomaly Detection in machine Learning
Discussion on Gradient Boosting for Regression and Classification problem
Recommender Systems in machine Learning
Decesion tree and Random Forest implementation and improve result with gradient boosting.
Neural Networks - understand basic of Deep Learning
Back Propagation in Deep learning -understanding fundamentals and implementation details.
Computer Vision
identify image and transformation on image 00:00:00
understand face recognition using deep learning 00:00:00
Implement Neural Networks to classify images -Deep Learning approach
Introduction to Tensor Flow- Deep learning for image classification
image classification and transformation of image 00:00:00
Natural Language Processing
Learn NLP using python package NLTK 00:00:00
understand processing text, classifying and tokenizing, 00:00:00
Analyzing sentence and similarity using cosine similarity 00:00:00
Create conversational Chatbot which uses google NLP API and Watson Assistant to find automated response 00:00:00

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