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 realworld scenarios.

Introduction to Machine Learning
 What is Machine Learning
 Supervised Learning
 Unsupervised Learning
 Semisupervised Learning

Machine Learning Terminologies
 Training, Validation, and Test data
 Regularization
 Hyperparameters

Machine Learning Model Lifecycle

BiasVariance Tradeoff
 Ovefitting, Underfitting, and Generalization
 How to solve the problem of overfitting and underfitting

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
 RSquare
 Adjusted RSquare
 Mean Squared Error
 Mean Absolute Error

Multicollinearity
 Effect of Multicollinearity in Linear Regression Models
 How to solve the problem of Multicollinearity

Ridge Regression
 How to choose optimal value of alpha

Lasso Regression
 How to choose optimal value of alpha
 Interpret the coefficients of Lasso regression model

Applying Linear Regression to RealWorld problems

Logistic Regression
 Difference between Linear and Logistic Regression
 What is Logit
 Relationship between Logit and Probability
 Odds Ratio
 Applying Logistic Regression to realworld problem
 How to interpret the coefficients of Logistic Regression Model

Imbalance Data Problem
 How do we solve imbalance data problem
 Oversampling
 Undersampling
 Synthetic Minority Oversampling Technique (SMOTE)

KNearest Neighbors (KNN)
 How to choose optimal number of neighbors
 Applying KNN to realworld problems

Decision Trees
 How does a decision tree work?
 Advantages of decision tree
 How to mitigate the problem of overfitting in decision tree
 Prepruning
 Selecting important features

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 realworld data
 Selecting important features

Introduction to Boosting
 How does a boosting algorithm work?
 Implement a gradient boosting model
 Implementing an Adaboost model
 Implement an XGBoost model

Hyperpameter Tuning
 Grid Search
 Randomized Search
 Automated Tuning using Hyperopt
 Cross Validation

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

Clustering Techniques  KMeans
 KMeans clustering
 How does KMeans work?
 How to choose optimal number of clusters
 Inertia
 Silhouette scores

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

Hierarchical Clustering
 Agglomerative clustering
 Divisive clustering
 How to implement agglomerative clustering
 Dendrogram
 How to choose optimal number of clusters using Silhouette Visualizer

Application of Clustering to RealWorld 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 realworld 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
 KNearest Neighbors
 Treebased 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 realworld data
 Principal Component Analysis
 Understand Clustering techniques like KMeans, DBSCAN, and Hierarchical Clustering
 How to choose optimal number of clusters
 Understand how to implement clustering algorithms on realworld 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