Foundations of Machine Learning
- Offered byCoursera
Foundations of Machine Learning at Coursera Overview
Duration | 22 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Foundations of Machine Learning at Coursera Highlights
- Earn a certificate from Fractal Analytics
- Add to your LinkedIn profile
- 7 assignments
Foundations of Machine Learning at Coursera Course details
- What you'll learn
- Construct Machine Learning models using the various steps of a typical Machine Learning Workflow
- Apply appropriate metrics for various business problems to assess the performance of Machine Learning models
- Develop regression and tree based Machine learning Models to make predictions on relevant business problems
- Analyze business problems where unsupervised Machine Learning models could be used to derive value from data
- In a world where data-driven insights are reshaping industries, mastering the foundations of machine learning is a valuable skill that opens doors to innovation and informed decision-making
- In this comprehensive course, you will be guided through the core concepts and practical aspects of machine learning. Complex algorithms and techniques will be demystified and broken down into digestible knowledge, empowering you to wield the capabilities of machine learning confidently
- By the end of this course, you will:
- 1. Grasp the fundamental principles of machine learning and its real-world applications
- 2. Construct and evaluate machine learning models, transforming raw data into actionable insights
- 3. Navigate through diverse datasets, extracting meaningful patterns that drive decision-making
- 4. Apply machine learning strategies to varied scenarios, expanding your problem-solving toolkit
- This course equips you with the foundation to thrive as a machine learning enthusiast, data-driven professional, or someone ready to explore the dynamic possibilities of machine learning
Foundations of Machine Learning at Coursera Curriculum
Introduction to Machine Learning
Gateway to the Course
Course and Instructor Introduction Video
Introduction to Problem Statement
How do we Make Predictions?
Methodology of Evaluating Predictions
Introduction to Data Division
Building Benchmark Models and Evaluating It
Introduction to Machine Learning
Applications of Machine Learning
Types of ML
Reading material - Understanding the Data
Introduction to ML
Building Your First Machine Learning (ML) Model for Synergix Solutions
ML Workflow
Tasks to be Performed
Combining Product Attribute Data with POS Data
Combining all the tables in the Dataframe
Understanding the Combined Data
Treating Missing Values - Part 1
Treating Missing Values Part 2
Outlier Detection and Treatment
Preparing the Dataset for Supervised and Unsupervised Models
Generative AI for Data Analysis
Introduction to KNN
Building a kNN model
Choosing the Optimal K
Different Ways to Calculate Distance
Problems with Distance Based Algorithm
Sklearn to build Optimal Process to Build an ML Model
Building a Knn classification model and evaluating it
Choosing the right K value
Bias and Variance
Building your first ML model
Preprocessing Data for Anova Insurance
Evaluating Prediction Models
Understanding Confusion Matrix and Accuracy
A deep dive into Precision, Recall and F1 Score
Understanding the AU-ROC curve
Why do we calculate RMSE
Understanding R2 Score and Adjusted R2 Score
Train-Test Split
Train-Test split ratio and limit
Cross validation
Implementing Cross validation
Benchmark Models
How to Evaluate a Model
Build and Evaluating KNN model for Anova Insurance
Linear and Logistic Regression
Introduction to Linear Regression
Significance of Slope and Intercept in the linear regression
How Model Decides The Best-Fit Line
Let’s Build a Simple Linear Regression Model
Model Understanding Using Descriptive Approach
Model Understanding Using Descriptive Approach - II
Model Building Using Predictive Approach
Introduction
Lines to Curves with Logistic Regression
Reading Between the Curves with Log Loss
Stats Model Summary
Feature Selection and Scaling
Predictive model in Logistic Regression
Linear regression
Logistic regression
Building a Logistic Model for Anova Insurance
Decision Trees for Synergix Solution
Introduction to Decision Trees
Let’s Visualize The Decision Tree
How Do Decision Trees Decide?
How Decision Trees Make Predictions?
Hands on: Building the Decision Tree Classification Model
Hyperparameters of Decision Trees
Hands on: Building the Decision Tree Classification Model - Part 2
Building a Decision Tree Regression Model
Handling Imbalanced Datasets
Handling Imbalanced Datasets - Hands on
Check your understanding for Decision Trees
Building Decision Trees for Anova Insurance
Introduction to Unsupervised Learning
Setting the Context
Choosing Clustering Algorithms
Solving our Problem using k-means - Part 1
Solving our Problem using k-means - Part 2
Finding optimal K value
Analysis and Insights Based on the Plot
Introduction to Hierarchical Clustering Analysis (HCA)
Solving our Problem using Hierarchical Clustering
Introduction to DBSCAN
Solving our Problem using DBSCAN Clustering
Course Summary
Applications of Clustering in the Real World
Unsupervised ML
KMeans Model for TapToBuy
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