VLR Training provides data science and Machine Learning online Training in Hyderabad by Industry Expert Trainers. We provide Machine Learning live projects to the students and also Every day Data Science Recorded sessions.
Data Science is growing by the second and the demand for Data Scientists is exponentially rising because of the sheer fact of how many companies rely on Data Scientists and how valuable an asset they’re to the company.
Data Science Machine Learning Online Training New Batch Schedule
 Course Duration:45 Days
 MonFri 7:30 am (IST)
 Mode of Training Online only
 Realtime Trainer
 After class, we will provide class recording for reference purpose
About Data Scientist and Machine Learners
Data Scientists are responsible for analysis of data with a goal of discovering insights which in turn can provide a competitive advantage or address a pressing business problem.
His role is to analyze data from different angles, determine what it means and then recommend ways to apply that data. They employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.
They are also responsible to communicate predictions and findings to management and IT departments through effective data visualizations and reports.
Machine Learning Online Training (Data science) Demo Video By Deepak
Data Science Machine Learning Demo Videos
Demo Video Deepak: https://youtu.be/Nb4SZIB26cY
01st Class video : https://youtube.be/A6jEcVzuaEA
02nd Class video : https://youtu.be/vJH4yrR7e6M
please subscribe to our you tube channel for more free Up coming Demo videos: https://goo.gl/G3cB5Q
Who can do this Machine Learning Online Training Course?
 Managers
 Data analysts
 Business analysts
 Operators
 Job Seekers
 End users
 Developers
 Freshers/Graduates
 IT professionals
 Those who are looking for Data Science career
Objectives Of Data Science Machine Learning Course
After the course completion of Data Science Machine Learning Trainingstudents will be able to:
 Be a part of a data science team to work on various other analytics projects
 Create statistical models and understand which insights can lead to accurate results
 Understand how advanced analytics developed to create competitive advantage
 Understand the roles and responsibilities of data scientists and BI analytics are different from each other
 Analyze several types of data using python
 Learn the tools and techniques with data transformation
 Machine Learning
What are the prerequisites for learning Data Science Machine Learning course?
Anybody can take this regardless of prior skills. Knowledge of statistics and mathematics is beneficial.
Why should you take the Data Science course?

 The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 – Forbes
 The average salary for a Data Scientist is $120k as per Glassdoor
 Businesses analyzing data will see $430 billion in productivity benefits over their rivals not analyzing data by 2020
Data science Machine Learning online Training Course Content
Part 1: Introduction to Data Science
 What is Data Science
 Role of a Data Scientist
 Responsibilities of a Data Scientist, day to day life activities
 Prerequisites for the course
Part2: Python Ecosystem for Machine Learning
 Introduction to Python, why it is the best choice for current Data Science projects
 Data types
 Operations
 Types of objects – Lists, Tuple, Dictionaries, and Strings
 List, Tuple, Dictionary, and String manipulations
 Control structures
 If statements,
 Loops – break, continue etc
 Functional programming concepts (Lambdas, Map, Reduce, Filter &List comprehension)
 Introduction to basic packages (NumPy, SciPy, Matplotlib, Pandas, Scikitlearn)
 Data frames and operations on it
Part3: Statistics, Probability, Linear algebra for Machine Learning
 Introduction to statistics
 Variable types
 Categorical variables
 Ordinal variables
 Continuous variables
 Descriptive statistics
 Mean
 Median
 Mode
 Range& IQR
 Variance
 Standard Deviation
 Correlation
 Covariance
 Inferential statistics
 Sample and population
 Sampling techniques (with and without replacement)
 Stratified sampling
 Hypothesis Testing
 Null and Alternative Hypothesis
 P value and Level of Significance
 TypeI and TypeII errors
 Chisquare test
 ZTest
 TTest
 FTest
 ANOVA (One & Two way)
 Distributions
 Normal
 Binomial
 Bernoulli
 Poisson
 Linear Algebra for Machine Learning
 The probability for Machine Learning
 Calculus for Machine Learning
Part4: Exploratory Data analysis
 Variable analysis (Univariate, Bivariate Analysis)
 Cleansing the data (Handling Missing Values, Outliers, etc)
 Understand data with Descriptive Statistics
 Understand data with Visualization
 Scatter Plots
 Box plots
 Histograms
 Heat Maps
Part5: Feature Engineering
 Prepare Your Data for Machine Learning.
 Feature transformation
 Feature creation
 Normalize Data
 Binarize Data (Label encoding, OneHot encoding, etc)
 Feature Selection for Machine Learning.
 Univariate Selection
 Recursive Feature Elimination
 Feature importance
 Imbalanced data handling techniques.
Part 6: Machine learning Algorithms
 Introduction to Machine Learning – Modeling Life Cycle with an example
 Introduction to Supervised, Unsupervised and Reinforcement Learning
 Understanding Problem line&selecting appropriate algorithm
 Preprocessing
 Building a model
 Evaluating results
 Finetuning hyperparameters
 Tips for best results
 Regression Algorithms
 Linear Regression
 Multiple linear regression
 Optimization algorithm – Gradient Descent
 Gradient Descent
 Batch Gradient Descent
 Stochastic Gradient Descent
 Tips for fast optimization
 Classification Algorithms
 Logistic Regression
 Decision Trees
 Naive Bayes
 Gaussian Naive Bayes
 KNearest Neighbors
 Support Vector Machine
 Unsupervised Algorithms
 Clustering Algorithms (Kmeans)
 Principal Component Analysis (PCA)
 Singular Value Decomposition (SVD)
Part 7: Ensemble Algorithms
 Bagging
 Basics
 Bootstrap Method
 Bootstrap Aggregation
 Example – Random Forest
 How Random forest works
 Hyperparameter tuning
 Variable importance
 Boosting
 Basics
 Example – GBM
 GBM implementation &Hyperparameter tuning
 Example – XGB (XGDT)
 GBM implementation & Hyperparameter tuning
 Comparison between GBM & XGB
Part 8: Evaluation & Performance metrics
 Splitting into train, validation and test sets
 Kfold Crossvalidation
 Leave One Out Crossvalidation
 What Techniques to Use When
 Classification model Evaluation Metrics (Confusion matrix, Accuracy, Precision, Recall &ROC)
 Regression model Evaluation Metrics (R^{2}, RMSE)
 Best evaluation tips
Part 9: Text Mining
 Basics
 Handling Stop words & Punctuations
 Bag of words
 TFIDF
 Ngram
 Implementation of Sentiment Analysis
Part 10: Neural Networks Basics
 What is Neural network and how it works
 Activation function
 Feedforward & Backpropagation Basics
 Implementation with Keras
Part 11: Recommendation System Basics
 Userbased Collaborative Filtering
 Itembased Collaborative Filtering
Part 12: Productionizing the Model
 Save and Load a model.
 Implementing model through Rest API
Part 13: EndtoEnd solution (Based on your interest)
VlrTraining Address:
PlotNo 126/b,2nd floor, Street Number 4,
Addagutta Society, Near Jntuh,Pragarthi Nagar Road,
Kukatpally, Hyderabad, Telangana 500072
Name: Data Science Machine Learning online Training
Telephone:9059868766
Opening hours:7 Am to 9 Pm (IST)
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Being a data scientist is a hugely rewarding career. Data science is becoming more necessary in a wide range of companies. It is becoming a popular career choice for many. So, what exactly does the job of a data scientist look like? In this article, you will know, what kind of skills we need to become a data scientist?
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