No. of Hours: 60
Certificate Validity: 2 Yr(s)
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, Services, Construction, Security, Retails 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.
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
– 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, data scientists and its scope
Introduction to Machine Learning
– How and what is ML with uses, types, applications
Python for Data Science
– numpy: Numerical Python
– pandas: Python data analytics with data frames object
– 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, Boosting and Stacking
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
– Methods of Detecting Outliers
Natural Language Processing
– Text mining with nltk and textblob
– Using Indian languages
Introduction to Neural Network
– MLP Classifier (ANN)
GUI Design for Projects
1. Operating System: Ubuntu 18.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum)
6 Hrs per Week (Saturday and Sunday) : Weekend Batch and
6 Hrs per Week (Wed, Thu and Fri) : Weekdays Batch
Total: 60 hrs.
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.
A duration of 30 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
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.