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


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

  • How to Implement machine learning in large Dataset
  • Understand Neural network  and Deep learning  with Reinforcement learning algorithm which are used for Artificial Intelligence product
  • Deep Learning used in Image classification,NLP(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 only training company from Bangalore to follow real time A.I courses.

We do cover all tools and framework for A.I such as Speech to Text, Reinforcement learning,Text data Mining ,Text data training using LSTMand NLP, Deep learning to train large image and video ,Chatbot with python etc.

A.I Engineer can master in following areas:

Machine learning, Probabilistic reasoning, Robotics, computer vision, and natural language processing.

Course Curriculum

Learn Python from Basic to Advance
Fundamentals of Python ,Numpy and Pandas
Python & NumPy​ ​Demonstrate introductory programming concepts using Python and NumPy as a tool to navigate data sources and collections 00:00:00
UNIX​ ​Utilize UNIX commands to navigate file systems and modify files 00:00:00
Descriptive Statistics​ ​Define and apply descriptive statistical fundamentals to sample data sets Intro to Plotting and Visualization 00:00:00
Git Hub:git​ ​Learn to keep track of changes and iterations using git version control from your terminal 00:00:00
Intro to Plotting and Visualization Practice plotting and visualizing data using Python libraries like matplotlib and Seaborn ​ Visualization 00:00:00
Project 2 ​Students will use Pandas to apply advanced NumPy and Python skills in order to clean, analyze, and test data from multiple messy datasets 00:00:00
Experiment Design​ ​Plan experimental study design with a well thought out problem statement and data framework Pandas & Pivot Tables ​Use Pandas to read, clean, parse, and plot data using functions such as boolean, indexing, math series, joins, and others 00:00:00
SciPy & Statsmodels​ ​Review statistical testing concepts (p-values, confidence intervals, lambda functions, correlation/causation) with SciPy and Stats models 00:00:00
Web Scraping​ ​Learn to scrape website data using popular scraping tools 00:00:00
Bootstrapping​ ​Practice resampling and building inferences about your data 00:00:00
Project 3​ ​Using a provided dataset, students will explore, clean, and model data, outlining their strategy and explaining their results 00:00:00
Gradient Descent​ ​Dive into the math and theory of how gradient descent helps to optimize loss function for regression models 00:00:00
Feature Selection​ ​Use feature selection to deepen your knowledge of study design and model evaluation 00:00:00
Regularization & Optimization Learn to apply regularization and optimization when evaluating model fit 00:00:00
K-Nearest Neighbors​ ​Begin to look at classification models through an application of the kNN algorithm 00:00:00
Project 4​ ​Clustering ​Students will scrape and model their own data using multiple methods, outlining their approach and evaluating any risks or limitations 00:00:00
Define clustering and it’s advantages and disadvantages from classification models 00:00:00
Ensemble Models​ ​Build and evaluate ensemble models, using decision trees, random forests, bagging, and boosting 00:00:00
NLP​ ​Get introduced to natural language processing through sentiment analysis of scraped website data. 00:00:00
Naive Bayes​ ​Learn how Naive Bayes can simplify the process of analyzing data for supervised learning algorithms 00:00:00
Time Series Analysis ​Analyze and model time series data using the ARIMA model in Pandas 00:00:00
PCA and Dimension Reduction with practical example on handwriting digits 00:00:00
SVM algorithm on regression and Classification problem with hyper parameter tuning 00:00:00
Understand Eigen Values and Eigen Vectors 00:00:00
Introduction to Deep Learning
Artificial Neural Network concept ,design and implementation 00:00:00
Feed Forward Neural Network and Back Propagation 00:00:00
Apply Neural Network to solve Employee Retention Dataset 00:00:00
Deep Dive into Artificial Inteligence
CNN Introduction,Building Fundamentals of how CNN works 00:00:00
OpenCV for Face Recognition and Video Analytics 00:00:00
Recurrent Neural Network and LSTM model with CNN for large text Data Training and learning pattern 00:00:00
Recommender Systems​ ​Build and apply basic recommender systems in order to predict on sample user data 00:00:00
Portfolio Development
​Work with career coaches to create and polish your portfolio for employers 00:00:00
Interview Prep​ ​Practice 20+ data science case studies to prep for job interviews 00:00:00

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