Data Science Machine Learning online Training Hyderabad

Machine Learning online Training and Data Science Real time Industry Expert demo video:

 

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
  • Mon-Fri 7:30 am (IST)
  • Mode of Training Online only
  • Real-time Trainer
  • After class, we will provide class recording for reference purpose

For more information  subscribe  to our channelhttps://goo.gl/G3cB5Q

 

Data Science machine learning online training hyderabad vlrtraining

02nd Class video( 04th Jan 2019)

 https://youtu.be/vJH4yrR7e6M

01st Class video( 3rd Jan 2019)

 https://youtu.be/A6jEcVzuaEA

Demo Video Deepak

 https://youtu.be/Nb4SZIB26cY

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.

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 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

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.prerequisites for learning Data Science Machine Learning vlrtraining

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

  1. What is Data Science
  2. Role of a Data Scientist
  3. Responsibilities of a Data Scientist, day to day life activities
  4. Prerequisites for the course

Part2:   Python Ecosystem for Machine Learning

  1. Introduction to Python, why it is the best choice for current Data Science projects
  2. Data types
  3. Operations
  4. Types of objects – Lists, Tuple, Dictionaries, and Strings
  5. List, Tuple, Dictionary, and String manipulations
  6. Control structures
  7. If statements,
  8. Loops – break, continue etc
  9. Functional programming concepts (Lambdas, Map, Reduce, Filter &List comprehension)
  10. Introduction to basic packages (NumPy, SciPy, Matplotlib, Pandas, Scikit-learn)
  11. Data frames and operations on it

Part3:   Statistics, Probability, Linear algebra for Machine Learning

  1. Introduction to statistics
  2. Variable types
  3. Categorical variables
  4. Ordinal variables
  5. Continuous variables
  6. Descriptive statistics
  7. Mean
  8. Median
  9. Mode
  10. Range& IQR
  11. Variance
  12. Standard Deviation
  13. Correlation
  14. Covariance
  15. Inferential statistics
  16. Sample and population
  17. Sampling techniques (with and without replacement)
  18. Stratified sampling
  19. Hypothesis Testing
  20. Null and Alternative Hypothesis
  21. P value and Level of Significance
  22. Type-I and Type-II errors
  23. Chi-square test
  24. Z-Test
  25. T-Test
  26. F-Test
  27. ANOVA (One & Two way)
  28. Distributions
  29. Normal
  30. Binomial
  31. Bernoulli
  32. Poisson
  33. Linear Algebra for Machine Learning
  34. The probability for Machine Learning
  35. Calculus for Machine Learning

Part4: Exploratory Data analysis

  1. Variable analysis (Uni-variate, Bi-variate Analysis)
  2. Cleansing the data (Handling Missing Values, Outliers, etc)
  3. Understand data with Descriptive Statistics
  4. Understand data with Visualization
    1. Scatter Plots
    2. Box plots
    3. Histograms
    4. Heat Maps

Part5:   Feature Engineering

  1. Prepare Your Data for Machine Learning
    1. Feature transformation
    2. Feature creation
    3. Normalize Data
    4. Binarize Data (Label encoding, One-Hot encoding, etc)
  2. Feature Selection for Machine Learning
    1. Univariate Selection
    2. Recursive Feature Elimination
    3. Feature importance
  3. Imbalanced data handling techniques.

Part 6:  Machine learning Algorithms

  1. Introduction to Machine Learning – Modeling Life Cycle with an example
    1. Introduction to Supervised, Unsupervised and Reinforcement Learning
    2. Understanding Problem line&selecting appropriate algorithm
    3. Preprocessing
    4. Building a model
    5. Evaluating results
    6. Finetuning hyperparameters
    7. Tips for best results
  2. Regression Algorithms
    1. Linear Regression
    2. Multiple linear regression
  3. Optimization algorithm – Gradient Descent
    1. Gradient Descent
    2. Batch Gradient Descent
    3. Stochastic Gradient Descent
    4. Tips for fast optimization
  4. Classification Algorithms
  5. Logistic Regression
  6. Decision Trees
  7. Naive Bayes
  8. Gaussian Naive Bayes
  9. K-Nearest Neighbors
  10. Support Vector Machine
  11. Unsupervised Algorithms
    1. Clustering Algorithms (K-means)
    2. Principal Component Analysis (PCA)
    3. Singular Value Decomposition (SVD)

Part 7:  Ensemble Algorithms

  1. Bagging
    1. Basics
    2. Bootstrap Method
    3. Bootstrap Aggregation
    4. Example – Random Forest
    5. How Random forest works
    6. Hyperparameter tuning
    7. Variable importance
  2. Boosting
    1. Basics
    2. Example – GBM
    3. GBM implementation &Hyperparameter tuning
    4. Example – XGB (XGDT)
    5. GBM implementation & Hyperparameter tuning
    6. Comparison between GBM & XGB

 

Part 8:  Evaluation & Performance metrics
  1. Splitting into train, validation and test sets
  2. K-fold Cross-validation
  3. Leave One Out Cross-validation
  4. What Techniques to Use When
  5. Classification model Evaluation Metrics (Confusion matrix, Accuracy, Precision, Recall &ROC)
  6. Regression model Evaluation Metrics (R2, RMSE)
  7. Best evaluation tips

Part 9: Text Mining

  1. Basics
  2. Handling Stop words & Punctuations
  3. Bag of words
  4. TF-IDF
  5. N-gram
  6. Implementation of Sentiment Analysis

Part 10: Neural Networks Basics

  1. What is Neural network and how it works
  2. Activation function
  3. Feedforward & Backpropagation Basics
  4. Implementation with Keras

Part 11: Recommendation System Basics

  1. User-based Collaborative Filtering
  2. Item-based Collaborative Filtering

Part 12: Productionizing the Model

  1. Save and Load a model.
  2. Implementing model through Rest API

Part 13: End-to-End solution (Based on your interest)

Machine learning online training (Data science) demo video by deepak

 

6 Comments

Add a Comment
  1. I’m interested in this will u explain me. Once again

    1. call us VLR Training 9059868766

    1. thank your sir, call now 9059868766 for data science

  2. I am interested data science

    1. thank you sir call now 9059868766 for data science

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

VlrTraining software training Kukatpally -Jntu © 2017 Frontier Theme