Sniffer Search job guaranteed course on Artificial Intelligence and Machine learning is 480 hours hands on training + project work on Deep learning and Product development of 380 Hours.

Once you completed all assignment and project, you will be qualified for
job market. We will help you get the best IT job within 7 month of training completion or 50 percent training fees will be refunded.


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Course Curriculum

Learn Python from Basic to Advance
Learn Python from Basic to Advance 00:00:00
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
Caption Project No :1
Work on Deep learning stack to increase accuracy in Text data understanding by Sniffer Search Chatbot product. You will be mentored to work on 100 hours in this project and write a comparison report of developing NLP learning stack using LSTM model and how it performs compared with Google Dialogflow and IBM NLP stack 00:00:00
Caption project 2:
Work on Building Analytics product for Sniffer Search which analyze amazon product sales for vendor like Hindustun unilever and help them boost sales and find bottleneck . You are expeected to work 200 hours in this project and improve algorithm and develop end to end code with mentors help 00:00:00
Capton project 3:
Work on Kaggle competition with mentor’s help on Geo spatial data, Market Analytics ,Healthcare Dataset 00:00:00

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