Machine Learning Blended

Blended

Machine Learning Blended

Teacher

Elenxia Learning Platform

Last Update:

July 18, 2023

Review:

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About Course

Starting Point For Your Career Path…
Our Mission
& Vision

We help undergrad and post grad students struggling to get industrial
experience with our Industry Grade Mentorship programs which help them
to become corporate-ready individuals and possess the skillset to take on
any challenges without any self-doubt.
Mission
Our aim is to become one of the most preferred
education technology platforms across the globe.
Vision
We envision a world in which each students receives the
effective, equitable, and engaging education they need to
reach their full and unique potential

You will learn how to use data science and machine learning with Python.
You will create data pipeline workflows to analyze, visualize, and gain insights from data.
You will build a portfolio of data science projects with real world data.
You will be able to analyze your own data sets and gain insights through data science.
Master critical data science skills.
Understand Machine Learning from top to bottom.
Replicate real-world situations and data reports.
Learn NumPy for numerical processing with Python.
Conduct feature engineering on real world case studies.
Learn Pandas for data manipulation with Python.
Create supervised machine learning algorithms to predict classes.
Learn Matplotlib to create fully customized data visualizations with Python.
Create regression machine learning algorithms for predicting continuous values.
Learn Seaborn to create beautiful statistical plots with Python.
Construct a modern portfolio of data science and machine learning resume projects.
Learn how to use Scikit-learn to apply powerful machine learning algorithms.
Get set-up quickly with the Anaconda data science stack environment.
Learn best practices for real-world data sets.
Understand the full product workflow for the machine learning lifecycle.
Explore how to deploy your machine learning models as interactive APIs.

Course Content

Induction Class

Introduction & Installation Python

Programming Elements

Conditions & Loops

Data Structures

Functions & Expection Handling

Add on Topics

Machine Learning

Linear Regression

Logistic Regression

Evaluation Metrices

KNN & SVM

Decision Tree & Ensemble Learning

Problem Statements

Unsupervised Learning-1

Unsupervised Learning-2

Add-on-Topics

Support

Instructor
Elenxia Learning Platform
Language
English

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Course Details

Machine Learning Blended

Teacher

Last Update:

Review:

About Course

What Will You Learn?

Course Content

Induction Class

Introduction & Installation Python

Background, Need & Importance Introduction & Installation Writing First Program I/O in Python

Programming Elements

Keywords & Variables Data Types – Numbers & Strings Operations in Python Hands on Implementation

Conditions & Loops

Indentation & Scopes If else & Elif Blocks Introduction to Loops For & While Loops Break & Continue Statements

Data Structures

Lists, Tuples, Sets Dictionaries CRUD Operations on Data Structures Building Rock Paper Scissor with Console

Functions & Expection Handling

Introduction to Function Positional & Keyword Arguments Return Statements Try, Catch & Finally Block

Add on Topics

File Handling – i/o Numpy Arrays Case Studies

Machine Learning

Understanding Prediction System Machine Learning v/s Deep Learning Forms of ML – Supervised & Unsupervised Regression & Classification

Linear Regression

Sklearn Library Prediction Pipeline Architecture of Linear Regression Best Fit Line & Prediction

Logistic Regression

Regression vs Classification Logistic Regression’s Architecture Bianry vs Multiclass Data

Evaluation Metrices

Loss Functions – RMSE, MSE, MAE Accuracy – Pros & Cons Confusion Metrices Precision, Recall & f1 Score

KNN & SVM

Hyperplanes & Support Vectors Architecture of SVM Classifiers K-Nearest Neighbors Classifiers Accuracy Comparisons

Decision Tree & Ensemble Learning

Tree Based Models Bagging & Boosting Concept of Ensemble Models Random Forest Classifiers

Problem Statements

Wine Quality Prediction Diabetes Prediction House Price Prediction Titanic Dataset

Unsupervised Learning-1

Need of Unsupervised Learning K-Means Clustering Training Kmeans

Unsupervised Learning-2

Mean Shifting Clustering K-Means v/s Mean Shift Clustering Industrial Use Cases of Unsupervised Learning.

Add-on-Topics

Hyperparameter Tuning Grid Search CV & Randomized Search CV Best Estimators & Best Params

Support

Tricks & Tips to clear interview questions Most preffered interview questions Resume preparation & Cover

Elenxia Learning Platform

English

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