CERTIFICATION COURSE

No. of Hours: 60

Certificate Validity: 2 Yr(s)

Introduction

This is an integrated program in Data Science and Machine Learning primarily designed for professionals pursuing career as Data Scientist, AI Engineers, Data Analyst and ML Engineers.

Our customized Data Science and Machine Learning Courses are designed by Data Scientists who are working on ML projects in our company. This program will help you achieve proficiency in Python programming language, data analysis, data pre-processing, machine learning algorithms, classification systems, regression modeling, clustering algorithms, recommender systems, and natural language processing to work on real world projects and case studies. These skills will help you prepare for the role of the Data Scientist.

Why to enroll for this course?

There is a huge requirement of Data Scientists in almost all domains including IT, Automobile, Telecommunication, Construction etc. and this is a major concern for Top MNCs organizations around the world. The major corporations are ready to pay top salaries for professionals with the good Data Science skills. This Data Science and Machine Learning Course equips with all the latest technologies Python, Data Analytics, and NLP. Thus you can easily take your career to the next level after completion of this certification course.

Prerequisite for learning

There are no special prerequisites required for Data Science and Machine Learning training. However, the knowledge of the following will make it easier:

  • Basic Mathematics
  • Simple Statistics
  • Knowledge of programming language
  • Business domain knowledge

Who will be our trainers ?

You can check the company and trainers profile here.

Course Contents

Introduction to Python
– The detailed overview of python as a programming language

Decision Making and Looping
– Using if, for and while control structures

Python Data Structures
– List, Tuple, Set and Dictionary

Python Functions
– Creating and using functions modules as well as packages

Object Oriented Programming
– Python classes, constructors and objects

Introduction to Data Science
– Detailed overview of data science and data scientists

Introduction to Machine Learning
– How and what is ML with uses

Python for Data Science
– numpy: Numerical Python
– pandas: Python data analytics with data frames object

Data Visualization
– matplotlib: creating different graphs
– seaborn: advanced visualization

Machine Learning Initiation
– Using ML in Python
– Data Pre-processing operations and their requirements

Machine Learning Algorithms: Regression
– Linear Regression
– Multiple Regression
– Polynomial Regression
– Decision Tree Regression
– Random Forest Regression
– SVM Regression

Machine Learning Algorithms: Classification
– Logistic Regression
– Decision Tree Classification
– Random Forest Classification
– SVM Classification
– KNN Classification
– Naive Bayes Classification

Ensemble Learning Techniques
– Bagging and Boosting

Dimensionality Reduction Techniques
– Principal Component Analysis
– Linear Discriminant Analysis

Machine Learning Algorithms: Clustering
– K-means Clustering
– Agglomerative Clustering
– Gaussian Mixture

Machine Learning Algorithms: Association Rule Mining
– Apriori Algorithm

Database operations
– SQL: MySQL Connectivity
– NoSQL: MongoDB Connectivity

Natural Language Processing
– Text mining with nltk and textblob
– Using Indian languages

Introduction to Neural Network
– MLP Classifier (ANN)

Big Data Analytics: Hadoop

GUI Design for Projects

Configuration:
1. Operating System: Ubuntu 18.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum)

Timings:
6 Hrs per Week (Saturday and Sunday) : Weekend Batch and
6 Hrs per Week (Wed, Thu and Fri) : Weekdays Batch
Total: 60 hrs.

Weekly Assignment:
1. After every weekend session you’ll need to solve assignments until next week.
2. On completion of 1 module, you are required to appear exam.
3. All exams will be taken either on Center / Off site. When Off-site you will submit assignments via mail.

Final Exam:
A duration of 15 days, will be given after completion of Course
The Final exam will be a conducted once, in case of Failure; exam can be reappeared after another 1 month

Opportunities:
As first Opportunity after Passing exams, you will deployed on a Well monitored Live Project.
You are required to resolve the Problem statement with Expected outputs, with that you will be receiving a token of Completion Letter of respective Project.