Machine Learning Certification Course - Institute of Business and Technology

Lesson1. Course Introduction

Course Introduction

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Accessing Practice Labs

00:00

Lesson 2. Introduction to AI and Machine Learning

Learning Objectives

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Emergence of Artificial Intelligence

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Artificial Intelligence in Practice

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Sci-Fi Movies with the Concept of AI

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Recommender Systems

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Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

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Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

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Definition and Features of Machine Learning

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Machine Learning Approaches

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Machine Learning Techniques

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Applications of Machine Learning: Part A

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Applications of Machine Learning: Part B

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Key Takeaways

00:00
Knowledge Check

00:00

Lesson 3. Data Preprocessing

Learning Objectives

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Data Exploration Loading Files: Part A

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Data Exploration Loading Files: Part B

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Demo: Importing and Storing Data

00:00
Practice: Automobile Data Exploration – A

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Data Exploration Techniques: Part A

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Data Exploration Techniques: Part B

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Seaborn

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Demo: Correlation Analysis

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Practice: Automobile Data Exploration – B

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Data Wrangling

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Missing Values in a Dataset

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Outlier Values in a Dataset

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Demo: Outlier and Missing Value Treatment

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Practice: Data Exploration – C

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Data Manipulation

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Functionalities of Data Object in Python: Part A

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Functionalities of Data Object in Python: Part B

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Different Types of Joins

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Typecasting

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Demo: Labor Hours Comparison

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Practice: Data Manipulation

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Key Takeaways

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Knowledge Check

00:00

Lesson 4. Supervised Learning

Learning Objectives

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Supervised Learning

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Supervised Learning- Real-Life Scenario

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Understanding the Algorithm

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Supervised Learning Flow

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Types of Supervised Learning: Part A

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Types of Supervised Learning: Part B

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Types of Classification Algorithms

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Types of Regression Algorithms: Part A

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Regression Use Case

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Accuracy Metrics

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Cost Function

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Evaluating Coefficients

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Demo: Linear Regression

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Practice: Boston Homes – A

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Challenges in Prediction

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Types of Regression Algorithms: Part B

00:00
Demo: Bigmart

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Practice: Boston Homes – B

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Logistic Regression: Part A

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Logistic Regression: Part B

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Sigmoid Probability

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Accuracy Matrix

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Demo: Survival of Titanic Passengers

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Practice: Iris Species

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Key Takeaways

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Knowledge check

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Health Insurance Cost

00:00

Lesson 5. Feature Engineering

Learning Objectives

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Feature Selection

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Regression

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Factor Analysis

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Factor Analysis Process

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Principal Component Analysis (PCA)

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First Principal Component

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Eigenvalues and PCA

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Demo: Feature Reduction

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Practice: PCA Transformation

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Linear Discriminant Analysis

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Maximum Separable Line

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Find Maximum Separable Line

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Demo: Labeled Feature Reduction

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Practice: LDA Transformation

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Key Takeaways

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Knowledge Check

00:00
Knowledge Check

00:00

Lesson 6. Supervised Learning Classification

Learning Objectives

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Overview of Classification

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Classification: A Supervised Learning Algorithm

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Use Cases of Classification

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Classification Algorithms

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Decision Tree Classifier

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Decision Tree Examples

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Decision Tree Formation

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Choosing the Classifier

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Overfitting of Decision Trees

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Random Forest Classifier- Bagging and Bootstrapping

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Decision Tree and Random Forest Classifier

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Performance Measures: Confusion Matrix

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Performance Measures: Cost Matrix

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Demo: Horse Survival

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Practice: Loan Risk Analysis

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Naive Bayes Classifier

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Steps to Calculate Posterior Probability: Part A

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Steps to Calculate Posterior Probability: Part B

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Support Vector Machines : Linear Separability

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Support Vector Machines : Classification Margin

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Linear SVM : Mathematical Representation

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Non-linear SVMs

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The Kernel Trick

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Demo: Voice Classification

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Practice: College Classification

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Key Takeaways

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Knowledge Check

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Classify Kinematic Data

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Lesson 7. Recommender Systems

Learning Objectives

00:00
Overview

00:00
Example and Applications of Unsupervised Learning

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Clustering

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Hierarchical Clustering

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Hierarchical Clustering Example

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Demo: Clustering Animals

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Practice: Customer Segmentation

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K-means Clustering

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Optimal Number of Clusters

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Demo: Cluster Based Incentivization

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Practice: Image Segmentation

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Key Takeaways

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Knowledge Check

00:00
Clustering Image Data

00:00

Lesson 8. Time series Modelling

Learning Objectives

00:00
Overview of Time Series Modeling

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Time Series Pattern Types: Part A

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Time Series Pattern Types: Part B

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White Noise

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Stationarity

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Removal of Non-Stationarity

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Demo: Air Passengers – A

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Practice: Beer Production – A

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Time Series Models: Part A

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Time Series Models: Part B

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Time Series Models: Part C

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Steps in Time Series Forecasting

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Demo: Air Passengers – B

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Practice: Beer Production – B

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Key Takeaways

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Knowledge check

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IMF Commodity Price Forecast

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Lesson 9. Ensemble Learning

Ensemble Learning

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Overview

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Ensemble Learning Methods: Part A

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Ensemble Learning Methods: Part B

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Working of AdaBoost

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AdaBoost Algorithm and Flowchart

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Gradient Boosting

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XGBoost

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XGBoost Parameters: Part A

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XGBoost Parameters: Part B

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Demo: Pima Indians Diabetes

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Practice: Linearly Separable Species

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Model Selection

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Common Splitting Strategies

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Demo: Cross Validation

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Practice: Model Selection

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Key Takeaways

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Knowledge Check

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Tuning Classifier Model with XGBoost

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Lesson 10. Recommender Systems

Learning Objectives

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Introduction

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Purposes of Recommender Systems

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Paradigms of Recommender Systems

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Collaborative Filtering: Part A

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Collaborative Filtering: Part B

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Association Rule Mining

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Association Rule Mining: Market Basket Analysis

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Association Rule Generation: Apriori Algorithm

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Apriori Algorithm Example: Part A

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Apriori Algorithm Example: Part B

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Apriori Algorithm: Rule Selection

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Demo: User-Movie Recommendation Model

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Practice: Movie-Movie recommendation

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Key Takeaways

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Knowledge Check

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Book Rental Recommendation

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Lesson 11. Text Mining

Learning Objectives

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Overview of Text Mining

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Significance of Text Mining

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Applications of Text Mining

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Natural Language ToolKit Library

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Text Extraction and Preprocessing: Tokenization

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Text Extraction and Preprocessing: N-grams

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Text Extraction and Preprocessing: Stop Word Removal

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Text Extraction and Preprocessing: Stemming

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Text Extraction and Preprocessing: Lemmatization

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Text Extraction and Preprocessing: POS Tagging

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Text Extraction and Preprocessing: Named Entity Recognition

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NLP Process Workflow

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Demo: Processing Brown Corpus

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Wiki Corpus

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Structuring Sentences: Syntax

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Rendering Syntax Trees

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Structuring Sentences: Chunking and Chunk Parsing

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NP and VP Chunk and Parser

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Structuring Sentences: Chinking

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Context-Free Grammar (CFG)

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Demo: Structuring Sentences

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Practice: Airline Sentiment

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Key Takeaways

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Knowledge Check

00:00
FIFA World Cup

00:00

About Course

Course Content

Lesson1. Course Introduction

Course Introduction

Accessing Practice Labs

Lesson 2. Introduction to AI and Machine Learning

Learning Objectives

Emergence of Artificial Intelligence

Artificial Intelligence in Practice

Sci-Fi Movies with the Concept of AI

Recommender Systems

Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

Definition and Features of Machine Learning

Machine Learning Approaches

Machine Learning Techniques

Applications of Machine Learning: Part A

Applications of Machine Learning: Part B

Key Takeaways

Knowledge Check

Lesson 3. Data Preprocessing

Learning Objectives

Data Exploration Loading Files: Part A

Data Exploration Loading Files: Part B

Demo: Importing and Storing Data

Practice: Automobile Data Exploration – A

Data Exploration Techniques: Part A

Data Exploration Techniques: Part B

Seaborn

Demo: Correlation Analysis

Practice: Automobile Data Exploration – B

Data Wrangling

Missing Values in a Dataset

Outlier Values in a Dataset

Demo: Outlier and Missing Value Treatment

Practice: Data Exploration – C

Data Manipulation

Functionalities of Data Object in Python: Part A

Functionalities of Data Object in Python: Part B

Different Types of Joins

Typecasting

Demo: Labor Hours Comparison

Practice: Data Manipulation

Key Takeaways

Knowledge Check

Lesson 4. Supervised Learning

Learning Objectives

Supervised Learning

Supervised Learning- Real-Life Scenario

Understanding the Algorithm

Supervised Learning Flow

Types of Supervised Learning: Part A

Types of Supervised Learning: Part B

Types of Classification Algorithms

Types of Regression Algorithms: Part A

Regression Use Case

Accuracy Metrics

Cost Function

Evaluating Coefficients

Demo: Linear Regression

Practice: Boston Homes – A

Challenges in Prediction

Types of Regression Algorithms: Part B

Demo: Bigmart

Practice: Boston Homes – B

Logistic Regression: Part A

Logistic Regression: Part B

Sigmoid Probability

Accuracy Matrix

Demo: Survival of Titanic Passengers

Practice: Iris Species

Key Takeaways

Knowledge check

Health Insurance Cost

Lesson 5. Feature Engineering

Learning Objectives

Feature Selection

Regression

Factor Analysis

Factor Analysis Process