Are you one of the many who dreams of becoming a Data Scientist or Machine Learning Engineer? Then you absolutely landed at the right place. At Digifine our Data Science and Machine Learning certification program absolutely trains you on how does Data Science and Machine Learning really work under the hood!

Under the Data Science & Machine Learning Certification Course we train you on how to use scientific methods, processes, systems and algorithms to extract insights from many structural and unstructured data.

Data Science is very closely related to Data Mining, Machine Learning and Big Data and hence the practical techniques that we train our students on are drawn from theories of many fields within the context of Statistical Analysis, Mathematics, Computer Science and Information Science.

Data science and Machine Learning are the growing field. A career as a Data Scientist and Machine Learning Engineer is ranked at the third best job for 2020 by Glassdoor and Linkedin.


Technologies and techniques

There are a variety of different technologies and techniques that are used for data science which depend on the application. More recently, full-featured, end-to-end platforms have been developed and heavily used for data science and machine learning.

-Linear Regression
-Advanced linear regression
-Decision Tree
-Ensembling/Random Forest and Stocking
-Boosting Algorithms
-Logistic Regression
-Clustering
-Recommender System
-Time Series Model


Who Should Attend This Training?

  • Students (irrespective of their field of education)
  • Working Professional
  • Entrepreneur
  • Freelancer
  • Anyone who wants to restart their career

Languages

Framework

  • Python
  • R Programming
  • TensorFlow
  • Jupyter Notebook
  • Apache

Register For The Upcoming Batch

Limited Seats Available!

 May 11, 2021
Multiple Batches Available.

Digifine Data Science & Machine Learning Master Program Modules

 
  • a)INTRODUCTION TO PYTHON:

  • → History of Python
    → Why to learn python
    → How is Python Different?
    → Installing Python
    → Tuples
    → Sets and Booleans
    → Loops
    → File I/O
  • b)PYTHON INTERPRETER:
  • → Using the interpreter
    → Integrated Development Environments (IDE) How to run
    → Python programs?
  • c)BASICS OF PYTHON:
  • → Variable
    → Keywords
    → Statements & Comments
    → Indentation
    → Data types
    → Static Typing vs Dynamic Typing
    → Input and output
  • d) OPERATORS:
  • → Arithmetic operator
    → Comparison Operator
    → Assignment Operator
    → Logical operator Bitwise operator
    → Identity Operator
  • e)CONTROL FLOW
  • → If statement
    → If - else
    → If – elif -else
    → Nested if - else while loop
    → For – in loop
    → Nested for loop → Nester while loop
    → Pass statement Break and continue
    → Arithmetic operator
    → Arithmetic operator
  • f)FUNCTIONS
  • → Basics Defining function
    → Function call Return statement Function with parameter and without parameter → Function parameters Call by value or call by reference → Local and global variable
    → Recursion, Anonymous (lambda) function User define functions
    → Examples
  • g)MODULES
  • → Defining module
    → How to create module
    → Importing module
  • h)PACKAGE
  • → Defining package
    → How to create package
    → Importing package
    → Installing third party packages
  • i)DATA COLLECTION
  • → List
    → Tuple
    → Dictionary
    → Set
  • j)ADDITIONAL CONCEPTS
  • → Numpy
    → Skipy
    → Pandas
    → Matpotlib
 
  • a)INTRODUCTION TO R
  • → Course Review
    → Understanding the versions of R & Choosing the Best Version
    → Downloading R Installing R Understanding R Environment
  • b) TRIGGERING R
  • → Complex Data Types in R: Vectors, Arrays, Matrices, Lists and Data Frame
  • c)PROGRAMMING R
  • → Getting Data into R from various Statistic Tools, Websites, CSVs, Databases etc
    → Familiarizing with Data Sets
    → Programming R with Control Flow & Looping con- structs
    → R Procedures
    → Getting Data into R from various Statistic Tools, Websites
  • b) DATA VISUALIZATION IN R
  • → Graphical Parameters in R Base
    → Base Plotting using Bar Charts, Box Plots, Histograms, Pie Charts & Use Cases
    → Understanding & Working with Graph Libraries.
 
  • a)INTRODUCTION TO MACHINE LEARNING
  • → Types of Machine learning
    → Data understanding : real life example & why Machine learning is future
    → Which skills are required for Machine learning
    → Discussion on different packages used for ML
  • b)DATA PREPROCESSING & REGRESSION TECHNIQUES
  • → Linear Regression Technique
    → Dataset with problem description
    → Non- Linear Regression Techniques Logistic Regression Technique
  • c)SUPPORT VECTOR MACHINE
  • → Support Vector machine
    → Introduction to Support Vector machine
    → Mathematical Approach
    → Practical application on R and Python
  • d)K- NEAREST NEIGHBORS
  • → Concept and theory
    → Distance functions: Euclidean, Minkowski
  • e)DECISION TREE
  • → Introduction to Decision tree
    → Significance of using Decision Tree
    → Different kinds of Decision Tree
  • f)RANDOM FOREST
  • → Random Forest
    → Theory and mathematical concepts Entropy and Decision Tree
    → Practical application on R and Python
  • g)CLUSTERING
  • → Introduction of clustering
    → K-mean clustering Practical application on R and Python
  • h)Deep Learning
  • → Scope of Deep Learning
    → Understanding Artificial Neural Network(ANN)
    → Introduction to python advance packages for Machine Learning: TensorFlow
-Clustering (Unsupervised)
-Recommender Systems
-Time Series Models
-Introduction to NLP
-Topic Modeling
-Sentiment Analysis
-Domain classification of customer messages

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