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. 

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.

Data Science Machine Learning Online Training Course Details

Course Duration

45 Days

Mon-Fri

8 am to 10 am (IST)

Mode of Training

Online

Prerequisites for Data Science Training

Everyone can get data science training.

  • Fresher’s/Graduates
  • Job Seekers
  • Managers
  • Data analysts
  • Business analysts
  • Operators
  • End users
  • Developers
  • IT professionals

Objectives Of Data Science Machine Learning Course

After the course completion of Data Science Machine Learning Training students 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

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

  • What is Data Science
  • Role of a Data Scientist
  • Responsibilities of a Data Scientist, day to day life activities
  • Prerequisites for the course
  • 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, Scikit-learn)
  • Data frames and operations on it
  • 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
  • Type-I and Type-II errors
  • Chi-square test
  • Z-Test
  • T-Test
  • F-Test
  • ANOVA (One & Two way)
  • Distributions
  • Normal
  • Binomial
  • Bernoulli
  • Poisson
  • Linear Algebra for Machine Learning
  • The probability for Machine Learning
  • Calculus for Machine Learning
  • Variable analysis (Uni-variate, Bi-variate 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
  • Prepare Your Data for Machine Learning.
    1. Feature transformation
    2. Feature creation
    3. Normalize Data
    4. Binarize Data (Label encoding, One-Hot encoding, etc)
  • Feature Selection for Machine Learning.
    1. Univariate Selection
    2. Recursive Feature Elimination
    3. Feature importance
  • Imbalanced data handling techniques.
  • 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
  • K-Nearest Neighbors
  • Support Vector Machine
  • Unsupervised Algorithms
  • Clustering Algorithms (K-means)
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • 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
  • Splitting into train, validation and test sets
  • K-fold Cross-validation
  • Leave One Out Cross-validation
  • What Techniques to Use When
  • Classification model Evaluation Metrics (Confusion matrix, Accuracy, Precision, Recall &ROC)
  • Regression model Evaluation Metrics (R2, RMSE)
  • Best evaluation tips
  • Basics
  • Handling Stop words & Punctuations
  • Bag of words
  • TF-IDF
  • N-gram
  • Implementation of Sentiment Analysis
  • What is Neural network and how it works
  • Activation function
  • Feedforward & Backpropagation Basics
  • Implementation with Keras
  • User-based Collaborative Filtering
  • Item-based Collaborative Filtering
  • Save and Load a model.
  • Implementing model through Rest API

Data Science Demo Videos By Deepak

Register Now for Data Science Live Demo