Foundations of Machine Learning
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Foundations of Machine Learning
This course is part of Fractal Data Science Professional Certificate
Instructor: Analytics Vidhya
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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
Skills you'll gain
- Model Optimization
- Machine Learning Methods
- Feature Engineering
- Regression Analysis
- Model Evaluation
- Anomaly Detection
- Unsupervised Learning
- Applied Machine Learning
- Decision Tree Learning
- Logistic Regression
- Model Training
- Data Preprocessing
- Supervised Learning
- Predictive Modeling
- Machine Learning
- Machine Learning Algorithms
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your Data Analysis expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Fractal Analytics
There are 6 modules in this course
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.
In this module, learners will unravel the magic of machine learning as they explore the significance of making predictions in various domains. They will gain a solid introduction to machine learning and its applications in different industries. The module will also cover essential concepts such as rule-based prediction and evaluation metrics, providing learners with a strong foundation for the rest of the course.
What's included
10 videos2 readings1 assignment
10 videos•Total 40 minutes
- Gateway to the Course•2 minutes
- Course and Instructor Introduction Video•2 minutes
- Introduction to Problem Statement•6 minutes
- How do we Make Predictions?•3 minutes
- Methodology of Evaluating Predictions•4 minutes
- Introduction to Data Division•3 minutes
- Building Benchmark Models and Evaluating It•6 minutes
- Introduction to Machine Learning•5 minutes
- Applications of Machine Learning•6 minutes
- Types of Machine Learning•4 minutes
2 readings•Total 40 minutes
- Syllabus - Foundation of Machine Learning•10 minutes
- Reading material - Understanding the Data•30 minutes
1 assignment•Total 30 minutes
- Introduction to ML•30 minutes
This module focuses on guiding learners through the complete workflow of building their first machine learning model. Learners will dive into data preparation, exploratory data analysis (EDA), and feature engineering techniques. They will learn to build a K-Nearest Neighbors (KNN) model, understand model evaluation, and explore crucial considerations for deploying an ML model in real-world applications.
What's included
19 videos2 assignments1 programming assignment
19 videos•Total 103 minutes
- ML Workflow•10 minutes
- Tasks to be Performed•6 minutes
- Combining Product Attribute Data with POS Data•8 minutes
- Combining all the tables in the Dataframe•9 minutes
- Understanding the Combined Data•4 minutes
- Treating Missing Values - Part 1•7 minutes
- Treating Missing Values Part 2•4 minutes
- Outlier Detection and Treatment•3 minutes
- Preparing the Dataset for Supervised and Unsupervised Models•4 minutes
- Generative AI for Data Analysis•7 minutes
- Introduction to KNN•2 minutes
- Building a kNN model•4 minutes
- Choosing the Optimal K•2 minutes
- Different Ways to Calculate Distance•7 minutes
- Problems with Distance Based Algorithm•4 minutes
- Sklearn to build Optimal Process to Build an ML Model•4 minutes
- Building a Knn classification model and evaluating it•12 minutes
- Choosing the right K value•2 minutes
- Bias and Variance•5 minutes
2 assignments•Total 75 minutes
- New Quiz•30 minutes
- Building your first ML model•45 minutes
1 programming assignment•Total 120 minutes
- Preprocessing Data for Anova Insurance•120 minutes
In this module, learners will delve into the intricacies of prediction models. They will explore evaluation metrics for both regression and classification models, gaining hands-on experience with practical implementations. The module will also cover data division techniques and benchmark performance, providing learners with a comprehensive understanding of how to effectively evaluate prediction models.
What's included
10 videos2 assignments1 programming assignment
10 videos•Total 60 minutes
- Understanding Confusion Matrix and Accuracy•6 minutes
- A deep dive into Precision, Recall and F1 Score•10 minutes
- Understanding the AU-ROC curve•5 minutes
- Why do we calculate RMSE•6 minutes
- Understanding R2 Score and Adjusted R2 Score•5 minutes
- Train-Test Split•8 minutes
- Train-Test split ratio and limit•3 minutes
- Cross validation•5 minutes
- Implementing Cross validation•6 minutes
- Benchmark Models•6 minutes
2 assignments•Total 90 minutes
- Practice Quiz•30 minutes
- How to Evaluate a Model•60 minutes
1 programming assignment•Total 60 minutes
- Build and Evaluating KNN model for Anova Insurance•60 minutes
In this module, learners will embark on a comprehensive exploration of regression techniques. From understanding the principles of linear and logistic regression to their practical application, they will gain valuable insights into predictive modeling. With a focus on real-world scenarios, they will learn how to make predictions, interpret results, and optimize models.
What's included
13 videos3 assignments1 programming assignment
13 videos•Total 71 minutes
- Introduction to Linear Regression•4 minutes
- Significance of Slope and Intercept in the linear regression•7 minutes
- How Model Decides The Best-Fit Line•4 minutes
- Let’s Build a Simple Linear Regression Model•6 minutes
- Model Understanding Using Descriptive Approach•10 minutes
- Model Understanding Using Descriptive Approach - II•8 minutes
- Model Building Using Predictive Approach•4 minutes
- Introduction•2 minutes
- Lines to Curves with Logistic Regression•5 minutes
- Reading Between the Curves with Log Loss•5 minutes
- Stats Model Summary•6 minutes
- Feature Selection and Scaling•6 minutes
- Predictive model in Logistic Regression•3 minutes
3 assignments•Total 150 minutes
- New Quiz•30 minutes
- Linear regression•60 minutes
- Logistic regression•60 minutes
1 programming assignment•Total 120 minutes
- Building a Logistic Model for Anova Insurance•120 minutes
In this module, learners will navigate the intricate paths of decision trees. Decision trees offer a transparent yet powerful approach to classification and regression tasks. Learners will delve into the mechanisms of decision tree construction, learn to handle overfitting through pruning and regularization, and discover the art of fine-tuning decision trees for optimal results.
What's included
10 videos2 assignments1 programming assignment
10 videos•Total 63 minutes
- Introduction to Decision Trees•4 minutes
- Let’s Visualize The Decision Tree•8 minutes
- How Do Decision Trees Decide?•8 minutes
- How Decision Trees Make Predictions?•4 minutes
- Hands on: Building the Decision Tree Classification Model•12 minutes
- Hyperparameters of Decision Trees•6 minutes
- Hands on: Building the Decision Tree Classification Model - Part 2•3 minutes
- Building a Decision Tree Regression Model•5 minutes
- Handling Imbalanced Datasets•7 minutes
- Handling Imbalanced Datasets - Hands on•6 minutes
2 assignments•Total 90 minutes
- Practice Quiz•30 minutes
- Check your understanding for Decision Trees•60 minutes
1 programming assignment•Total 120 minutes
- Building Decision Trees for Anova Insurance•120 minutes
In this module, learners will unlock the mysteries of unsupervised machine learning as they dive into clustering techniques. They will discover the power of KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) in grouping similar data points together. They will also explore how unsupervised learning revolutionizes data exploration, customer segmentation, and anomaly detection.
What's included
11 videos1 reading2 assignments1 programming assignment
11 videos•Total 54 minutes
- Setting the Context•3 minutes
- Choosing Clustering Algorithms•5 minutes
- Solving our Problem using k-means - Part 1•10 minutes
- Solving our Problem using k-means - Part 2•3 minutes
- Finding optimal K value•8 minutes
- Analysis and Insights Based on the Plot•2 minutes
- Introduction to Hierarchical Clustering Analysis (HCA)•3 minutes
- Solving our Problem using Hierarchical Clustering•6 minutes
- Introduction to DBSCAN•7 minutes
- Solving our Problem using DBSCAN Clustering•6 minutes
- Course Summary•2 minutes
1 reading•Total 10 minutes
- Applications of Clustering in the Real World•10 minutes
2 assignments•Total 90 minutes
- Practice Quiz•30 minutes
- Unsupervised ML•60 minutes
1 programming assignment•Total 120 minutes
- KMeans Model for TapToBuy•120 minutes
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Reviewed on Mar 9, 2025
The theory explanation and the coding are very well organized and presented .It helped me understand things better.
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