Thursday, August 06, 2020

best Data Science training in marathahalli, bangalore

Course Duration: 30 hours
Attend 3 Free Classes to Check Training Quality
100% Real Time Practical Training with Placement Assistance
(Trained by 15+ years experienced working professionals )

Data Science Training Course Content

Python Goal

Get an overview of the python which is required to work on data science

Python Objectives

At the end of this Module, you should be able understand the following topics

  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Importing packages
  • If else
  • Loops
  • Comprehensions
  • Functions
  • Map
  • Filter
  • Reduce
  • Numpy
  • Pandas
  • Merging,querying,aggregating
  • Assignments for practice

R Goal

Get an overview of the R which is required to work on data science

R Objectives

At the end of this Module, you should be able to understand the following topics

  • Introduction
  • Basic operations in R
  • Vectors
  • Factors
  • Matrices
  • Data frames
  • Lists
  • Logical and Relational operators
  • Conditional Statements
  • Loops
  • Functions
  • Apply Family

Introduction

  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing R and R Studio (MAC & Windows)
  • Installing Python and Anaconda (MAC & Windows)

Part Data Preprocessing

  • Welcome to Part  – Data Preprocessing
  • Get the dataset
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • And here is our Data Preprocessing Template!
  • Quiz  Data Preprocessing

Part Regression

  • Welcome to Part  – Regression

Simple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Simple Linear Regression Intuition –   
  • Simple Linear Regression in Python –  
  • Simple Linear Regression in R –  
  • Quiz  Simple Linear Regression

Multiple Linear Regression

  • How to get the dataset
  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition –   
  • Multiple Linear Regression in Python –   
  • Multiple Linear Regression in Python – Backward Elimination – Preparation
  • Multiple Linear Regression in Python – Backward Elimination –  !
  • Multiple Linear Regression in Python – Backward Elimination –  Solution
  • Multiple Linear Regression in R –   
  • Multiple Linear Regression in R – Backward Elimination –  !
  • Multiple Linear Regression in R – Backward Elimination –  Solution
  • Quiz  Multiple Linear Regression

Polynomial Regression

  • Polynomial Regression Intuition
  • How to get the dataset
  • Polynomial Regression in Python –  
  • Python Regression Template
  • Polynomial Regression in R –   
  • R Regression Template

Support Vector Regression (SVR)

  • How to get the dataset
  • SVR in Python
  • SVR in R

Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in Python
  • Decision Tree Regression in R

Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python
  • Random Forest Regression in R

Evaluating Regression Models Performance

  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance – ‘s Final Part
  • Interpreting Linear Regression Coefficients
  • Conclusion of Part  – Regression

Part Classification

  • Welcome to Part  – Classification

Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in Python –  
  • Python Classification Template
  • Logistic Regression in R –   
  • R Classification Template
  • Quiz  Logistic Regression

K-Nearest Neighbors (K-NN)

  • K-Nearest Neighbor Intuition
  • How to get the dataset
  • K-NN in Python
  • K-NN in R
  • Quiz  K-Nearest Neighbor

Support Vector Machine (SVM)

  • SVM Intuition
  • How to get the dataset
  • SVM in Python
  • SVM in R
    • SVMzip

Kernel SVM

  • Kernel SVM Intuition
  • Mapping to a higher dimension
  • The Kernel Trick
  • Types of Kernel Functions
  • How to get the dataset
  • Kernel SVM in Python
  • Kernel SVM in R

Naive Bayes

  • Bayes Theorem
  • Naive Bayes Intuition
  • Naive Bayes Intuition (Challenge Reveal)
  • Naive Bayes Intuition (Extras)
  • How to get the dataset
  • Naive Bayes in Python
  • Naive Bayes in R

Decision Tree Classification

  • Decision Tree Classification Intuition
  • How to get the dataset
  • Decision Tree Classification in Python
  • Decision Tree Classification in R

Random Forest Classification

  • Random Forest Classification Intuition
  • How to get the dataset
  • Random Forest Classification in Python
  • Random Forest Classification in R

Evaluating Classification Models Performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy Paradox
  • CAP Curve
  • CAP Curve Analysis
  • Conclusion of Part  – Classification

Part Clustering

  • Welcome to Part  – Clustering

K-Means Clustering

  • K-Means Clustering Intuition
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in Python
  • K-Means Clustering in R
  • Quiz  K-Means Clustering

Hierarchical Clustering

  • Hierarchical Clustering Intuition
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • How to get the dataset
  • HC in Python –   
  • HC in R –   
  • Quiz  Hierarchical Clustering
  • Conclusion of Part  – Clustering

Part Association Rule Learning

  • Welcome to Part  – Association Rule Learning

Apriori

  • Apriori Intuition
  • How to get the dataset
  • Apriori in R –   
  • Apriori in Python

Eclat

  • Eclat Intuition
  • How to get the dataset
  • Eclat in R
    • Eclatzip

Part Reinforcement Learning

  • Welcome to Part  – Reinforcement Learning

Upper Confidence Bound (UCB)

  • The Multi-Armed Bandit Problem
  • Upper Confidence Bound (UCB) Intuition
  • How to get the dataset
  • Upper Confidence Bound in Python –   
  • Upper Confidence Bound in R –  

Thompson Sampling

  • Welcome to Part  – Natural Language Processing
  • How to get the dataset
  • Natural Language Processing in Python –   
  • Challenge
  • Natural Language Processing in R –  
  • Natural Language Processing in R –  
  • Challenge

Part Natural Language Processing

  • Thompson Sampling Intuition
  • Algorithm Comparison UCB vs Thompson Sampling
  • How to get the dataset
  • Thompson Sampling in Python –  
  • Thompson Sampling in Python –  
  • Thompson Sampling in R –  
  • Thompson Sampling in R –  

Part Deep Learning

  • Welcome to Part  – Deep Learning
  • What is Deep Learning?

Artificial Neural Networks

  • Plan of attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • How to get the dataset
  • Business Problem Description
  • ANN in Python –   – Installing Theano, Tensorflow and Keras
  • ANN in R –   
  • ANN in R –   (Last )

Convolutional Neural Networks

  • Plan of attack
  • What are convolutional neural networks?
  • – Convolution Operation
  • (b) – ReLU Layer
  • – Pooling
  • – Flattening
  • – Full Connection
  • Summary
  • Softmax & Cross-Entropy
  • How to get the dataset
  • CNN in Python –  
  • CNN in R

Part Dimensionality Reduction

  • Welcome to Part  – Dimensionality Reduction

Principal Component Analysis (PCA)

  • How to get the dataset
  • PCA in Python –   
  • PCA in R –  

Linear Discriminant Analysis (LDA)

  • How to get the dataset
  • LDA in Python
  • LDA in R

Kernel PCA

  • How to get the dataset
  • Kernel PCA in Python
  • Kernel PCA in R

Part Model Selection & Boosting

  • Welcome to Part  – Model Selection & Boosting

Model Selection

  • How to get the dataset
  • k-Fold Cross Validation in Python
  • k-Fold Cross Validation in R
  • Grid Search in Python –   
  • Grid Search in R

XGBoost

  • How to get the dataset
  • XGBoost in Python –   
  • XGBoost in R
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Regular : 2 Hour per dayFast Track : 2 – 3 Hours per day: 20 days
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Online Training : AvailableClass Room Training : Available
Course Fee : Talk to our Customer SupportDuration : 30 Hours
What is the batch size?

SDLC training providing the limited batch size, so we can provide quality teaching. If you want to get trained individually, we are also providing.

How you people will help for the Job?

SDLC training providing the 100% job assistance and mock interviews.

How you people will help in the projects?

SDLC training is providing the training with live projects and real-time practice.

How you people will provide the doubt clarification?

SDLC training providing the 24/7 interact access with faculties and after course also engagement between the faculties and students.

What are the extra services?

SDLC training providing the back up classes, soft skill training, interview skills workshop and resume preparation assistance.

How you people will help to enhance the students knowledge?

SDLC training providing the topics wise ppts, case studies, assignments and doubt solving.

 

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Theory

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Hands-on live projects

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Google Reviews

  • review rating 5  good trainers, good enviroment to study. i have completed AWS, the trainer is friendly and teaches things in the simplest way so that any one can understand easily. also they provide jobs after completion of the course. so, go for this institute .

    thumb Rajesh kumar Biswal
    6/29/2020

    review rating 4  Really helpful tutors and best training institute for beginners from different field, to start the career in AWS Trainning .Including theory and practical classes ,helped to develop indepth knowledge in front end and Cloud architecture.Manav sir always help us for clearing doubt any time and by giving various example and videos.I learnt many things during these period.DEMO Classes available for various domain which is also very intresting.

    thumb sagar nayak
    6/29/2020
  • review rating 5  very good trainer available for sap fico at SDLC Real-time Trainer with Good price for online

    thumb sunita das
    6/24/2020

    review rating 4  Very good training institute for beginner as well as professional and give very strong platform both career wise and knowledge wise.

    thumb Kumar Pankaj
    6/24/2020
  • review rating 5  I enjoyed the course and I feel satisfied talking the course .The procedure was perfectly organised .The tutor was extremely kind of supportive .The trainer were also helpful & friendly..

    thumb Santosh Sahoo
    6/24/2020

    review rating 5  The quality is good and environment is friendly. The timings are manipulative as per ones convenience that is a plus point. Faculty here is also good.Good communication between student and Faculty. I can ask whatever question I have regarding the subject I’m getting trained for at any working hour directly to the faculty.

    thumb Shaah Rukh Mansoori
    6/23/2020
  • review rating 5  my personal experience is very good with with tutors and support staffs, they are very helpful throughout the the learning and other aspects. Growth of every student is there motive, thnak you SIR and Santosh Sir

    thumb Ashish Raj
    6/15/2020

    review rating 4  We gain plenty of knowledge from each class, friendly environment , Serenity. Also want to add Nikhil Sir who is taking class for Java and Selenium truely knowledgeable person. He clears all concept in easy way.

    thumb preeti das
    4/25/2020

Best Data Science Training in Bangalore

Data Science Course

Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, the activity of cleansing, preparing and aligning the data.

SDLC training institute providing the Data Science real-time online training classes, classroom training classes for the weekend and regular batches. Get JOB with our free Placement Assistance Program.

There are various sectors where you can g too.

  • Next generation mobile apps
  • Business functions
  • Gaming
  • Communication

How we will start the course?

  • Learn from basics
  • Practice coding
  • Set your algorithm carefully
  • Trace your codes on paper
  • Read sources on Data Science regularly

At end of the course?

  • Trainees will understand the core concepts of Data Science.
  • Participants will have an understanding of how to create and implement algorithms.
  • Candidates will have detailed knowledge about Data Science.
  • Real-time project experience.
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Features of SDLC​

  • Limit the batch size so we can provide personal attention to everyone in the session
  • Real-time practice
  • Live projects
  • 24/7 interact access with faculties
  • Experienced and passionate trainers
  • After course engagement
  • We give topics wise ppt, case studies, assignments and doubt solving
  • 100% job assistance
  • 24/7 support
  • Classroom training, Online training and Corporate training
  • Student can attend their missed classes
  • Soft skill training, interview skills workshop, resume preparation assistance
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