Supervised ML for Predictive Analytics

Name: Supervised ML for Predictive Analytics
Rating: 4.8 (7 reviews)

Predictive Analytics | Linear Regression | Cost function | Gradient descent for predictive Modeling

Created byInfinity Learning

Last updated 8/2025

English

English [Auto],

What you'll learn

Introduction to machine learning
Types of machine learning
Deep dive in supervised machine learning
Deep dive in supervised machine learning
Introduction to NumPy
Introduction to Pandas
Introduction to house price prediction
Introduction to Linear Regression

Course content

4 sections • 24 lectures • 1h 44m total length

Introduction to course1:28
Discover the fundamentals of machine learning and supervised learning for predictive analytics. Understand linear regression and its implementation, and evaluate models for real-world applications across industries.
Introduction to Machine Learning1:32
Explore how machine learning, a subfield of artificial intelligence, uses data-driven algorithms to build models for tasks like image categorization and speech recognition, with supervised, unsupervised, and reinforcement learning.
Machine Learning in Detail1:22
Explore the details of machine learning, including speech recognition, natural language processing, computer vision, medical outcomes analysis, robot control, and computational biology, along with trends and learning paradigms.
Types of Machine Learning2:09
Explore the three main types of machine learning—supervised, unsupervised, and reinforcement—covering labeled data for classification and predicting outcomes, unlabeled data for clustering, and reward-based decision learning.
Quiz:1 Knowledge of Machine Learning
Supervised Machine Learning1:47
Explore supervised machine learning using a training set and cross-validation. Fit models, minimize loss, and predict results with neural networks, linear regression, logistic regression, svm, knn, and random forest.
Why there is Need of Classification?2:07
Discover why classification matters in supervised learning, illustrated by credit scoring and applications in pattern recognition, face and speech recognition, and medical diagnosis.
Regression and its Importance1:20
Examine regression and its importance through linear regression models, predict car prices from attributes and theta parameters, and explore applications in car navigation and robotic arm kinematics.
Introduction to NumPy2:37
discover numpy, a fundamental Python library for scientific computing and multi-dimensional arrays. install via pip, create arrays and matrices, perform sorting, arithmetic, transpose, and basic linear algebra.
Introduction to Panadas1:59
Explore pandas, the fast open-source data analysis tool built on Python, and learn to read csv files into dataframes, use df.describe for statistics, and plot charts with df.plot.
Quiz:2 Pandas Functions
Coming Weeks Lectures and Labs1:15
Explore supervised learning and linear regression through hands-on labs using pandas, NumPy, and Jupyter notebooks, culminating in real-world house price prediction models and model comparisons.
Simple and Complex Models2:10
Contrast simple linear models with complex nonlinear models like neural networks and decision trees; highlight data and resource needs in regression and fruit classification examples.

Lab 1 Introduction to Jupyter Notebook1:45
Explore Python basics and a tour of Jupyter notebooks in supervised ML for predictive analytics, including markdown vs code cells, and practice by implementing an assignment that squares a number.
Lab 2 Introduction to Pandas Functions3:22
Learn to import pandas, load a csv, inspect data with describe and info, plot with matplotlib, and sort by age to explore blood pressure data for predictive analytics.
Introduction to House Price Prediction6:15
Explore how linear regression predicts house prices from factors like size and land price, using a best-fit line and y = mx + c.
Lab 3 Creating Dataframe for actual Price5:09
Learn linear regression with one variable using numpy and matplotlib, build a house size versus price dataset, and plot training examples to visualize the regression slope.
Similarities in equations4:18
Explore similarities between the slope of a line and linear regression in machine learning, covering features, weights, bias, and how predictions adjust with data.
Introduction to Linear Regression model function3:29
Explore linear regression for predictive analytics, linking a target variable to predictors with a simple linear model using weight and bias, illustrated by house prices and ad spend.
Lab 4 Linear Regression Model Implementation in small data6:40
Define a linear regression model f_wb(x) = w x + b, compute predictions with numpy, and visualize predicted versus actual data using matplotlib.
Original Prediction1:22
Use a linear regression model to predict house price from a 1200 sq ft house, with x in thousands of square feet, weight w, and bias b.
Apply Linear Regression on dataset

Concepts of Linear Algebra, Probability and Gradient Descent8:34
Here is a link to go to the article, where you'll find the pdf of mathematical concepts which will used in machine learning.
Steps:
Go to the GitHub by clicking on the providing link
https://github.com/AhmedShafique313/sml_udemy
then go the week5 folder in this repository
only the provided pdf in the folder week5
Deep dive in Linear Regression model8:50
Explore linear regression in depth, including simple and multiple regression, model equations, and the ordinary least squares method to minimize squared errors and identify the best fit line.
Cost Function2:47
Explore the linear regression cost function defined by squared error and standard error of the estimate, to measure how well the regression line fits data, with Python steps.
Implementing linear Regression in Python6:48
Implement linear regression in Python with sklearn, clean data and handle missing values, visualize with histograms and scatter plots, and train, test, and predict salaries from experience.

Requirements

Hands on practice and practical exercises on NumPy and Pandas functions
Little bit programming experience in python language.
Famailier with programming

Description

Dive into the fundamentals of predictive modeling and predictive analytics with our comprehensive Linear Regression course. Master the essential techniques to unleash the potential of supervised machine learning.

Course Overview:

This course provides a comprehensive introduction to machine learning, focusing specifically on supervised learning techniques with a deep dive into logistic regression. Students will explore the fundamental concepts of machine learning, understand the principles behind supervised learning, and master the implementation and application of logistic regression models.

In this comprehensive course, you'll delve into the fundamental concepts and practical applications of linear regression, a cornerstone of supervised machine learning. From understanding the underlying mathematics to implementing regression models in Python, you'll gain the skills needed to analyze relationships between variables, make accurate predictions, and extract valuable insights from your data. Whether you're a beginner or seasoned data scientist, this course provides a structured approach to mastering linear regression and leveraging its predictive capabilities across various industries and domains.

With hands-on exercises, real-world examples, and expert guidance, you'll be equipped to tackle regression problems with confidence and drive data-driven decisions with precision. Embark on your journey to becoming a proficient practitioner in predictive modeling with linear regression today!

Instructor of this course from Services By IEO:

Ahmed Shafique

Specialized in supervised machine learning specially in regression and classification

Specialized in deep learning in Deep Neural Networks

Ready to unlock the secrets of predictive analytics? Enroll now and master linear regression for accurate data-driven decisions! Take the first step towards becoming a proficient data scientist today!

Who this course is for:

Understanding of basic mathematics operation
This course is for those who are beginners and want to go in field of data analyst
This course is also for those who are beginners but want to work on realitic world problems like house price prediction

Supervised ML for Predictive Analytics

What you'll learn

Explore related topics

Course content

Introduction11 lectures • 20min

Linear Regression8 lectures • 32min

Errors and prediction using mathematical concepts in Linear Regression Model4 lectures • 27min

Types of Linear Regression Model1 lecture • 12min

Requirements

Description

Who this course is for: