Home Data Science Machine Learning

Data Science & Machine Learning Master's program

Data Science & Machine Learning Master's program online training you will master the technique of how Machine Learning is deployed for Data Science, work with Pandas library for Data Science, data cleaning, data visualization, Machine Learning, advanced numeric analysis, etc. along with real-world projects and case studies.

  • 45000
  • 50000
Download Brochure
  • Course Includes
  • Live Class Practical Oriented Training
  • 90 + Hrs Instructor LED Training
  • 50 + Hrs Practical Exercise
  • 25 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
Report

Have Query ?

What you will learn

  • Perform scientific & technical computing using SciPy package & its sub-packages such as Integrate, Optimize, Statistics,...
  • In-depth understanding of supervised & unsupervised learning models like linear & logistic regression, clustering, dimen...
  • Learn how to implement the unsupervised learning algorithms, including deep learning, clustering, & recommendation syste...
  • Develop an understanding classification data and models
  • Gain important experiences into signs, pictures, and sounds with SciPy, scikit-picture, and OpenCV
  • Analyze information with Bayesian or frequentist insights (Pandas, PyMC, & R), & gain from genuine information through A...

Requirements

  • The candidates willing to join the introduction to machine learning training should have a prior acquaintance on fundamentals of of programming & matrix algebra.

Description

|| About Data Science & Machine Learning Training Course

Master’s in Data Science and Machine Learning online training course will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Data Science with Python course helps you learn the python programming required for Data Science. Data Science & Machine Learning mainly focuses on the enhancement and development of the computer programs, which has the property to get changed when it comes in the interaction to the new data. However, this is a kind of artificial intelligence, the Introduction to Machine Learning course enlightens the candidates with the algorithms that proves to be helpful for the IP professionals in analysing the data set with ease. In the training modules algorithms such as: regression, clustering, classification, and recommendation have been introduced, all these helps the candidates in supervising the advanced data programming techniques. 

 

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets.                  

 

 

 

Course Content

Live Lecture

·         Introduction of Python

·         The Companies using Python

·         Different Applications where Python is used

·         Discuss Python Scripts on UNIX/Windows

·         Values, Types, Variables

·         Operands and Expressions

·         Conditional Statements

·         Loops

·         Command Line Arguments

·         Writing to the screen

·         Sequences and File Operations

·         Python files I/O Functions

·         Numbers

·         Strings and related operations

·         Tuples and related operations

·         Lists and related operations

·         Dictionaries and related operations

·         Sets and related operations

·         Functions, OOPs, Modules, Errors and Exceptions

·         Functions

·         Function Parameters

·         Global Variables

·         Variable Scope and Returning Values

·         Lambda Functions

·         Object-Oriented Concepts

·         Standard Libraries

·         Modules Used in Python

·         The Import Statements

·         Module Search Path

·         Package Installation Ways

·         Errors and Exception Handling

·         Handling Multiple Exceptions
Live Lecture

·      Introduction To Statistic

·      Distributions and Hypothesis Tests

·      Distributions of One Variable

·      Hypothesis Testing

·      Typical Analysis Procedure

·      Data Screening and Outliers

·      Normality Check

·      Hypothesis Concept, Errors, p-Value, and Sample Size

·      Chi Square

·      ANOVA

·      Practical Exercise              

Live Lecture

·      Pandas Introduction

·      Series

·      Data Frames

·      CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise              

Live Lecture

·      Mathematical Computing with Python (NumPy)

·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise