Machine Learning Specialization
This program covers the fundamentals of both supervised and unsupervised machine learning methodologies, and includes quizzes and practical assignments to reinforce the concepts learned in real-world scenarios.
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Introduction to Machine Learning
- What is Machine Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
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Machine Learning Terminologies
- Training, Validation, and Test data
- Regularization
- Hyperparameters
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Machine Learning Model Lifecycle
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Bias-Variance Tradeoff
- Ovefitting, Underfitting, and Generalization
- How to solve the problem of overfitting and underfitting
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Introduction to Linear Regression
- Simple and Multiple Linear Regression
- Error Analysis in Linear Regression
- How to interpret the coefficients of Linear Regression Models
- Metrics for measuring Linear Regression
- R-Square
- Adjusted R-Square
- Mean Squared Error
- Mean Absolute Error
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Multicollinearity
- Effect of Multicollinearity in Linear Regression Models
- How to solve the problem of Multicollinearity
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Ridge Regression
- How to choose optimal value of alpha
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Lasso Regression
- How to choose optimal value of alpha
- Interpret the coefficients of Lasso regression model
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Applying Linear Regression to Real-World problems
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Logistic Regression
- Difference between Linear and Logistic Regression
- What is Logit
- Relationship between Logit and Probability
- Odds Ratio
- Applying Logistic Regression to real-world problem
- How to interpret the coefficients of Logistic Regression Model
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Imbalance Data Problem
- How do we solve imbalance data problem
- Oversampling
- Undersampling
- Synthetic Minority Oversampling Technique (SMOTE)
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K-Nearest Neighbors (KNN)
- How to choose optimal number of neighbors
- Applying KNN to real-world problems
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Decision Trees
- How does a decision tree work?
- Advantages of decision tree
- How to mitigate the problem of overfitting in decision tree
- Pre-pruning
- Selecting important features
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Bagging Classifiers
- Introduction to Ensemble models
- Introduction to Random Forest
- How does a random forest model work?
- Advantages of random forest
- Implement random forest models on real-world data
- Selecting important features
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Introduction to Boosting
- How does a boosting algorithm work?
- Implement a gradient boosting model
- Implementing an Adaboost model
- Implement an XGBoost model
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Hyperpameter Tuning
- Grid Search
- Randomized Search
- Automated Tuning using Hyperopt
- Cross Validation
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Dimensionality Reduction Techniques
- Principal Component Analysis (PCA)
- How does PCA work?
- How to choose optimal number of components
- Explained Variance Ratio
- How to interpret Principal Components
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Clustering Techniques - K-Means
- K-Means clustering
- How does K-Means work?
- How to choose optimal number of clusters
- Inertia
- Silhouette scores
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Clustering Techniques - DBSCAN
- DBSCAN
- epsilion (eps)
- Min_samples
- Core points
- Border points
- Noise points
- How does DBSCAN work?
- How to choose optimal value of eps and min_samples
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Hierarchical Clustering
- Agglomerative clustering
- Divisive clustering
- How to implement agglomerative clustering
- Dendrogram
- How to choose optimal number of clusters using Silhouette Visualizer
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Application of Clustering to Real-World problems
- Product Segmentation
- Customer Segmentation
In this course, you will learn how to build robust machine learning models like regression and classification models that leverage statistical assumptions.
You will learn how to deal with the problem of overfitting through model regularization. You will also learn how to deal with the problem of multicollinearity that exists among features in data. You will learn several techniques to tackle imbalance data problem. You will apply all the techniques taught on 4 real-world projects, thereby building your data science portfolio.
What you will learn
- Understand the foundation of Machine Learning
- Machine Learning Terminologies - Training, Validation, Test data, Regularization
- Machine Learning Model Lifecycle
- Understand the concept of Overfitting, Underfitting, and Generalization of Machine Learning Models
- Understand the meaning and effect of Multicollinearity
- Linear Regression
- Metrics for measuring the performance of linear regression models
- Lasso and Ridge regressions
- Logistic Regression
- Understand performance measures like Precision, Recall, F1, Confusion Matrix, AUC scores
- Understand Imbalance data problem and how to deal with it using SMOTE
- K-Nearest Neighbors
- Tree-based models like Decision Trees, Random Forests, Gradient Boosting, and XGBoost
- Hyperparameter Tuning techniques like Grid and Randomized Search
- Understand how to build a robust supervised learning models on real-world data
- Principal Component Analysis
- Understand Clustering techniques like K-Means, DBSCAN, and Hierarchical Clustering
- How to choose optimal number of clusters
- Understand how to implement clustering algorithms on real-world data
How students rated this courses
4.8
(Based on 5 reviews)
Reviews
Omolola Olasunkanmi 19 Jan, 2024 - 10:05 AM
5
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Ayeni Bukola 20 Jan, 2024 - 7:48 AM
The class was well understood.
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Ayeni Bukola 10 Feb, 2024 - 8:08 AM
had a lovely lecture
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Omolola Olasunkanmi 24 Feb, 2024 - 6:48 AM
Method of teaching is topnotch
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Omolola Olasunkanmi 07 Mar, 2024 - 3:48 AM
Excellent delivery of all the modules.
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Transcript from the "Introduction" Lesson
Course Overview [00:00:00]
My name is John Deo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.
Introduction [00:00:16]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Why Take This Course? [00:00:37]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
A Look at the Demo Application [00:00:54]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Summary [00:01:31]
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Course - Frequently Asked Questions
How this course help me to design layout?
My name is Jason Woo and I work as human duct tape at Gatsby, that means that I do a lot of different things. Everything from dev roll to writing content to writing code. And I used to work as an architect at IBM. I live in Portland, Oregon.
What is important of this course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Why Take This Course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
Is able to create application after this course?
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
We'll dive into GraphQL, the fundamentals of GraphQL. We're only gonna use the pieces of it that we need to build in Gatsby. We're not gonna be doing a deep dive into what GraphQL is or the language specifics. We're also gonna get into MDX. MDX is a way to write React components in your markdown.
What's included
- Certificate
- 21 Modules
- Live Classes
- Lifetime access
