Recorded & Live Sessions
The training programs consist of pre-recorded video lectures as well as live sessions in the form of Q&A sessions, lab sessions, project sessions etc.
Hands-on Learning
Students will work on building models for regression and real world projects on supervised and unsupervised learning.
Flexible Schedule
Students can start and complete the training as per their own schedule and convenience.
Completion Certificates
All students who register for certification programs and complete the training will receive industrial training certificate from Euclid Labs.
Training Program Highlights
About Training Program
Machine Learning is a fairly new paradigm but a game-changer in the field of software development. It has become an integral part of many commercial applications and research projects.
Are you ready to start your path to becoming a Data Scientist!
This course is designed for both absolute beginners without any previous knowledge of Data Science, Machine Learning and various algorithms.
In this training program, you will get an overview of lifecycle of Data Science, skillsets required to become a data scientist. We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:
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Programming with Python
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NumPy for matrice
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Using pandas Data Frames to solve complex tasks
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Handling CSV files using Pandas
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Loading files from Seaborn directory
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Using Matplotlib and Seaborn for data visualizations
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Implementing machine learning using sklearn
This course provides students with knowledge, hands-on experience of state-of-the-art machine learning techniques such as:
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Logistic regression
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K Nearest Neighbors (KNN)
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K Means Clustering
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Decision Trees
As a part of the training program, we are going to build 4 projects from scratch using real world datasets. This kind of experience is highly valued by prospective employees.
Training Content
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Introduction
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Environment Setup
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Overview
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Anaconda distribution
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Jupyter Notebook
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Python Crash Course
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Data type
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Data Structure
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List
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Tuples
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Dictionary
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Python Libraries
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Numpy
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Pandas
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Matplotlib
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Seaborn
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Data Analysis
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Machine Learning: Workflow and types
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Exploratory data analysis
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Introduction to Machine Learning
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Supervised Learning: Logistic regression
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Project: Predict the survival rates of the titanic based on the passenger features.
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Supervised Learning: Naive Bayes classification
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Project: Build an e-mail spam classifier using Naive Bayes classification Technique
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Supervised Learning: Linear regression
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Project: E-commerce company to focus on mobile app or website for better user experience.
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Unsupervised Learning: K Means Clustering
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Project: Develop a fraud detection classifier using Machine Learning Technique
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