Data Science Training in Madurai

Python / R / SQL

Python: Environment Setup And Essentials,Mathematical Computing With Python (Numpy), Scientific Computing With Python (Scipy),Data Manipulation With Python (Pandas)

INTRODUCTION What is R?,Why R?,Installing R,R environment,How to get help in R,R console and Editor,Packages in R,IMPORTING AND EXPORTING IN R,Loading data from file(Text,Csv,Excel),Loading data from clipboard,Connecting MySQL in R,How to remove lines while importing,Saving R data format,Exporting in R(Excel,Text),LOOPS,DATA MANIPULATION,DATA CLEANING PROCESS:,VISUALIZATION IN R,ADVANCED CONCEPT

SQL DATABASE: Understanding Database as a service,SQL Database is not SQL Server,Advance Capabilities,Benefits of SQL database,Scaling SQL database,Backup Options,Performance Model,Security,SQL Reporting,SQL Data Sync,SQL Federation,Pricing

Data Science & Analytics Overview

DATA SCIENCE OVERVIEW Data Science, Data Scientists, Examples of Data Science, Python for Data Science

DATA ANALYTICS OVERVIEW Introduction to Data Visualization Processes in Data Science, Data Wrangling, Data Exploration, and Model Selection, Exploratory Data Analysis or EDA, Data Visualization, Plotting, Hypothesis Building and Testing

STATISTICAL ANALYSIS AND BUSINESS APPLICATIONS

Introduction to Statistics,Statistical and Non-Statistical Analysis,Some Common Terms Used in Statistics,Data Distribution: Central Tendency, Percentiles, Dispersion,Histogram,Bell Curve,Hypothesis Testing,Chi-Square Test,Correlation Matrix,Inferential Statistics

PROJECT : AWESOME DATA SCIENCE

An open source Data Science repository to learn and apply towards solving real world problems.

Data Visualizations

Introduction to Data Visualization, Python Libraries, Plots, Matplotlib Features: Line Properties Plot with (x, y),Controlling Line Patterns and Colors

• Set Axis, Labels, and Legend Properties,Alpha and Annotation
• Multiple Plots,Subplots,types of Plots and Seaborn.

Machine Learning

Basic of Probability and Statistics,Basic of Linear Algebra,Basic of Laplace’s Transformation

MACHINE LEARNING

Introduction, What is ML?, Types of ML,Machine learning examples,Well defined machine learning problem, Supervise Learning, Regression,Classification,Decision Tree Learning,Artificial Neural Network,Bayesian Learning,instance-Based Learning,Genetic Algorithms,Learning Sets of Rules,Principal Components Analysis,Support Vector Machines, Kernel,HMM (Forward-Backward, Veterbi, EM for Learning), Neural Network, Reinforcement Learning

PROJECT 1: Fare predictiom for uber- Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies and building its next-generation model

Natural Language Processing NLP

Welcome to the Ultimate NLP Practitioner Training (associate),History of NLP and Definitions:The 5 Principals for Success,The Keys to an Achievable Outcome,Logical levelsSMART Goals,Pre-suppositions of NLP,The Prime Directives of the Unconscious mind,Rapport – The NLP practitoners secret persuasion weapon,Sensory acuity – in NLP – Neuro Linguistic Programming,Representational Systems,Eye Patterns,Submodalities,Linguistic Pre-suppositions,Hierarchy of ideas,NLP Metaphors,The Meta Model,Anchoring in NLP – Neuro-Linguistic ProgrammingStrategiesReframingParts Integration:The Fast Phobia Model5 Step Sales Process,NLP in Context,NLP Negotiation model,Articles on NLP Interventions,Bonus section

PROJECT : A Natural Language Processing (NLP), Machine Learning and Data Mining project, which will automate the screening process before hiring a professional or can be used in psychiatry to check effectivity of patient therapy.

Deep Learning

Overview of Tensor flow, Why Tensor flow?, Graphs and Sessions, Check out Tensor Board

Operations; Basic operations, Constants, variables, Control dependencies, Data pipeline

Fundamentals of deep learningWhat is deep learning?, What is Neural Network, The mathematical building blocks of neural networks, What is Neural networks, History Neural NetworkHebbian learning,Backpropagation,Hardware-based designs,Contests,Convolutional networks,Models,Components of an artificial neural network,Neural networks as functions,Learning,Learning paradigms,Learning algorithms,Optimization,Algorithm

Algorithm in code, Phase 1: propagation, Phase 2: weight update, Pseudocode, Extension

Adaptive learning rate Inertia,Modes of learning Variants,Group method of data handling

Convolutional neural networks,Long short-term memory,Deep reservoir computing,Deep belief networks,Large memory storage and retrieval neural networks,Stacked (de-noising) auto-encoders,Deep stacking networks,Tensor deep stacking networks,Spike-and-slab RBMs

Compound hierarchical-deep models,Deep predictive coding networks,Networks with separate memory structures,Multilayer kernel machine,Neural architecture search

Fundamentals of machine learning,Deep learning in practice,Deep learning for computer vision with OpenCv2 with Python,Deep learning for text and sequences,What is text and Sequences,What is Encoding and Decoding ext Analysis,Advanced deep-learning best practices,Generative deep learning, Deep learning in Application, Deep learning With OpenCV, Deep learning With Caffe, DeepLearning, Deep learning With Tensorflow

Deep learning With Theano,Deep learning With PyTorch

PROJECT : Project of several deep-learning algorithms using pure Python, then Keras, and scikit Deep neural networks have garnered tremendous excitement in recent years thanks to their superior learning capacity in the presence of abundant data resources. However, collecting an exhaustive dataset covering all possible scenarios is often slow, expensive, and even impractical. The goal of this project is to devise a new learning framework .