Accelerate your career with project based Machine Learning certification

Learn Through Projects | Complete Internship | Get The Job

Program Details



2 Months


To be announced soon!

Start Date

4 days in a week


Internship | Projects


Why To Enroll?

Build your resume, GitHub, Kaggle, LinkedIn profile 

Get internship Certificate in Machine Learning

Get Dedicated and personalised doubt solving sessions

Prepare for interviews with us

Learn through projects. Complete 10 projects in 2 months

Learn latest tools and technologies in Data Science

Machine Learning Training Certification Brochure

Best-in-class content to give you real time industry experience in the field of data science.

Get Your hands dirty with following projects

Training Curriculum

In this training program, you will learn everything you need to build Machine Learning projects, how to apply machine learning concepts to real-world projects. This training program will cover the traditional approach of building machine learning solutions to model deployment techniques with Flask and Streamlit integration.

Course Curriculum

- Data types, Python data structures, conditional statements, functions, Lambda, Map, Filter, File Handling - Python libraries: Pandas, Numpy, Matplotlib

Statistics and Basic Mathematics
- Basic Mathematics: Linear algebra, matrices - Descriptive Statistics: Mean, Median, Mode, Central Tendency, Std deviations, outliers - Inferential Statistics: Hypothesis testing, p values, confidence interval, type 1, type 2 error

SQL and Databases
- Basic operations of SQL queries and database structures as SQL and NoSQL database

Classical Machine Learning
- Exploratory Data Analysis: Data Cleaning, Data Visualization, Feature Engineering Methods - Data visualization using Tableau - Supervised and unsupervised Machine Learning: Regression, assumptions, mathematical understanding - Decision tree, Random Forest, Bagging, Boosting Algorithms, KNN Algorithm, Model Evaluation and - optimization techniques for regression and classification techniques - Confusion Matrix, Precision, Recall, F1 Score, Class Imbalance Handling Methods - Clustering methods

Deployment Techniques
- Understanding the model deployment for Classification, regression and clustering problems, - Rest API integration - Deployment on Heroku by flask API and Streamlit

Cloud and advanced ML
- Basics of cloud operations on AWS, AZURE and GCP - MLOps basics