Artificial Intelligence Master Certificate Course

Sniffer Search provides Best Artificial intelligence and Machine Learning course with Mentor .
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

Overview of Data Science:
What is Data Science ,Machine Learning and Deep Learning 00:00:00
Overview of Artificial Intelligence
Artificial Intelligence Basics 00:00:00
Skills required to become A.I engineer,Data Scientist
How to become Data Scientist 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
Introduction to Numpy and Numpy operation
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
Introduction to Machine learning and Linear Regression 00:00:00
Gradient Descent of Machine Learning
Logistic regression -a Machine learning approach to solve binary problem -Discussion of Titanic Data Set
Basic understanding of Logistic Regression and problem solving 00:00:00
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
Details of KNN and K means algorithm with problem statement 00:00:00
Anomaly Detection in machine Learning
Introduction to Naive Bayes Algorithm and example of classification of structured and unstructured data
Naive Bayes algorithm with problem solving 00:00:00
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
Build Artificial Intelligence application using Open CV,Deep learning ,Speech to Text and NLP/NER
Build Artificial Intelligence Application using CNN and Reinforcement Learning
Build Question -Answer Chatbot using Artificial Intelligence

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