Machine Learning Training Program | Let The Data Confess

TRAINING

Accelerate your career with project based Machine Learning certification

Learn Through Projects | Complete Internship | Get The Job

Program Details

Beginners

Audience

2 Months

Duration

To be announced soon!

Start Date

4 days in a week

Timings

Internship | Projects

Perks

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

Predict the fraudulent firm based on historical risk factors
Predict performance of marketing campaign
Predict liver disease patients
Predict facebook ad performance of a company
Identify types of seeds for agriculture use
Predict the income of an employee of the company
Predict if a customer is going to take the services of the company
Predict vehicle routing and scheduling problems
Build the recommenation system for products on e commerce website
Predict the uber fare for a given time

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

Python
- 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