Mastering Machine Learning: Course-1

Name: Mastering Machine Learning: Course-1
Rating: 4.4 (246 reviews)

Taking First Step Towards Machine Learning

Created byParteek Bhatia

Last updated 8/2020

English

English [Auto],

What you'll learn

Machine Learning
Python
Regression
Classification
Unsupervised Machine Learning

Course content

4 sections • 17 lectures • 1h 52m total length

Beginning with Machine Learning10:17
Explore how machine learning powers search ranking, personalization, email spam filtering, and recommendations. Understand the evolving definitions of learning from data and experience that drive modern algorithms.
Regression: Supervised Machine Learning11:35
Learn how supervised machine learning uses labeled data to predict continuous values, exemplified by predicting house prices with regression, input attributes, and model fitting to improve accuracy.
Classification: Supervised Machine Learning8:43
The lecture covers supervised learning and classification, using tumor size and other input attributes to predict benign or malignant cancer, and explains binary versus multiclass classification and attribute selection.
Unsupervised Machine Learning10:08
Explore unsupervised machine learning by clustering data using input attributes when no output label exists, and compare it with supervised learning that relies on labeled data for prediction.

Install Anaconda and Spyder to Implement Simple Linear Regression0:03
Dataset for Implementation of Simple Linear Regression0:02
Step 1: Importing the Libraries and the Dataset9:19
Master simple linear regression by fitting a straight line to predict a stipend from research experience, using the equation y = mx + c and minimizing the cost function.
Step 2a: Train Test Split and Its Need for Implementation of SLR16:45
Learn why train-test split matters for simple linear regression, and implement two-thirds training and one-thirds testing in Python using train_test_split with random_state to guard against overfitting.
Step 2b: Performing Train Test Split for Implementation of SLR8:29
Split data into training and testing sets to build a simple linear regression model, train on 20 research experience values, test on 10, and compare predicted salaries with actual salaries.
Step 3: Building the Model for Implementation of SLR4:42
Import the linear regression class from Escalon's linear_model library and fit the model on X_train and y_train to learn the relationship between X and Y for future predictions.
Step 4: Predicting the Values on Test Data for Implementation of SLR3:58
Apply the trained simple linear regression model to predict values on X_test, then compare predicted results with actual values and prepare for visualization-based evaluation in the next session.
Step 5: Visualization of Results for Implementation of SLR11:14
Visualize results of a simple linear regression model using matplotlib, plotting training and test data with scatter plots and the fitted regression line, and labeling axes.
Step 6: Evaluating the model using Regression metrics for Implementation of SLR7:21
Evaluate a simple linear regression model using regression metrics like mean absolute error, mean squared error, and RMSE in Python to compare actual versus predicted values.

Requirements

Zero prior technical experience is required! All you need is a passion to learn and experiment new things.

Description

This course will be a part of series of Free ML Courses to become an expert of ML. Presenting here its First Course on Machine Learning for becoming expert of ML.

This course presents the concepts of Supervised Machine Learning, Unsupervised Machine Learning, Regression and Classification.

It covers implementation of Simple Linear Regression.

Who this course is for:

Machine Learning Enthusiast
Students taking Machine Learning Course
Professionals working in the area of Data Analytics
Students preparing for placement tests and interviews
Excellent course for all the students of Non-IT Branches as it provides the basic knowledge of ML without any prerequisite

Mastering Machine Learning: Course-1

What you'll learn

Explore related topics

Course content

Introduction to Machine Learning4 lectures • 41min

Simple Linear Regression2 lectures • 9min

Beginning with Python2 lectures • 1min

Implementing Simple Linear Regression9 lectures • 1hr 2min

Requirements

Description

Who this course is for: