Artificial Intelligence and Machine Learning course in India online

Sniffer Search provides Best Artificial intelligence and Machine Learning training in bangalore .
We follow very high end learning model to understand math behind artificial Intelligenc
We follow implementation of machine learning with 13 sample Data set using Python package and library(numpy,scipy,pandas,scikit-learn and matplotlib).

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



At the end of  the course Artificial Intelligence training in India  which is online you will learn:


Python basic and Python Package for machine learning  implementation

Python package for Data exploration and visualization

Exploratory Data Analysis with tons of sample data

All supervised and unsupervised learning algorithm

Neural network (one of the most popular Artificial Intelligence modelling  used for deep learning)

Deep Learning used in Image classification,NLP,automated car (latest Artificial Intelligence algorithm)

Natural language processing with Artificial Intelligence

Why Learn from us:

The  Artificial Intelligence course in India and abroad are provided with high bandwidth internet connectivity and machine learning training is a complete solution to understand Data Science ,Machine learning and This training on Artificial Intelligence  online is best in India  and  there is one to one training to deliver concept and practical  by best developer and data scientist as trainer .

We also provide freshers opportunity to work on  project and product development after completion of Artificial Intelligence and machine learning training in India online

The training in provided online and offline candidate specially from location Hennur,Hebbal ,Kalyan Nagar,Banaswadi ,Nagawara,Manyatta Tech Park and  horamavu .

This world will replace all low IQ job with Artificial Intelligence as there are software used to replace in normal banking operation in 1990. So there are requirement of million of techie to contribute to artificial Intelligence  to make our daily life easy .For example :medical system diagnose automated using Artificial Intelligence,help critical illness identify with artificial Intelligence ,autonomous vehicle using artificial Intelligence,more  Robot to help human like cops,industry,house cleaning etc using artificial intelligence.

  • 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  hands on with real time example 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
Artificial Intelligence: in Games 00:00:00
Artificial Intelligence: in Finance 00:00:00
Skills required becoming a data scientist Source of Big Data Steps involved in Data Analytics 00:00:00
Big Data, Data Analytics and Python
Different sectors using Big Data Data Analytics and Python Python library and package for Data Science Advantage of using python for Data Science Data Wrangling and Data Exploration 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
Exploratory Data Analysis
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
Introduction to Neural Network Representation -Deep learning approach with Neurons and Brain,Example and Intuition
Non Linear hypothesis in Machine Learning
Back Propagation in Deep learning -understanding fundamentals and implementation details.
Cost Function Evaluating model in machine Learning
Unsupervised Learning -K means Algorithm in machine learning
Anomaly Detection in machine Learning
Implement Reinforcement Learning for Artificial Intelligence
Discussion on Gradient Boosting for Regression and Classification problem
Recommender Systems in machine Learning
Introduction to Tensor Flow- Deep learning for image classification

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