This online course offers an in-depth overview of machine learning topics, including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. You will also learn how to use Python to draw predictions from data.

Interactive learning with Jupyter notebooks integrated labs

Dedicated mentoring session from faculty of industry experts

Delivery Mode

Blended

This course consists of self-paced learning and live virtual classroom

Prerequisites

This course requires an understanding of:

Statistics

Mathematicss

Python programming

Python for Data Science

Math Refresher

Statistics for Data Science

Target Audience

Data analysts looking to upskill

Data scientists engaged in prediction modeling

Any professional with Python knowledge and interest in statistics and math

Business intelligence developers

Key Learning Outcomes

Master the concepts of supervised and unsupervised learning, recommendation engine, and
time series modeling

Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises

Acquire thorough knowledge of the statistical and heuristic aspects of machine learning

Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python

Validate machine learning models and decode various accuracy metrics.

Improve the final models using another set of optimization algorithms, which include boosting &and bagging techniques

Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning

Course Curriculum

Lesson 01 - Course Introduction

Course Introduction

Lesson 02 - Introduction to AI and Machine Learning

Learning Objectives

The 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

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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

Lesson 03 - Data Preprocessing

Learning Objectives

Data Exploration: Loading Files

Demo: Importing and Storing Data

Practice: Automobile Data Exploration I

Data Exploration Techniques: Part 1

Data Exploration Techniques: Part 2

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Demo: Correlation Analysis

Practice: Automobile Data Exploration II

Data Wrangling

Missing Values in a Dataset

Outlier Values in a Dataset

Demo: Outlier and Missing Value Treatment

Practice: Data Exploration III

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

Lesson-end project: Storing Test Results

Lesson 04 - Supervised Learning

Learning Objectives

Supervised Learning

Supervised Learning- Real-Life Scenario

Understanding the Algorithm

Supervised Learning Flow

Types of Supervised Learning – Part A

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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 I

Challenges in Prediction

Types of Regression Algorithms - Part B

Demo: Bigmart

Practice: Boston Homes II

Logistic Regression - Part A

Logistic Regression - Part B

Sigmoid Probability

Accuracy Matrix

Demo: Survival of Titanic Passengers

Practice: Iris Species

Key Takeaways

Lesson-end Project: Health Insurance Cost

Lesson 05 - Feature Engineering

Learning Objectives

Feature Selection

Regression

Factor Analysis

Factor Analysis Process

Principal Component Analysis (PCA)

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Eigenvalues and PCA

Demo: Feature Reduction

Practice: PCA Transformation

Linear Discriminant Analysis

Maximum Separable Line

Find Maximum Separable Line

Demo: Labeled Feature Reduction

Practice: LDA Transformation

Key Takeaways

Lesson-end Project: Simplifying Cancer Treatment

Lesson 06 - Supervised Learning: Classification

Learning Objectives

Overview of Classification

Classification: A Supervised Learning Algorithm

Use Cases

Classification Algorithms

Decision Tree Classifier

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Decision Tree Formation

Choosing the Classifier

Overfitting of Decision Trees

Random Forest Classifier- Bagging and Bootstrapping

Decision Tree and Random Forest Classifier

Performance Measures: Confusion Matrix

Performance Measures: Cost Matrix

Demo: Horse Survival

Practice: Loan Risk Analysis

Naive Bayes Classifier

Steps to Calculate Posterior Probability: Part A

Steps to Calculate Posterior Probability: Part B

Support Vector Machines: Linear Separability

Support Vector Machines: Classification Margin

Linear SVM: Mathematical Representation

Non-linear SVMs

The Kernel Trick

Demo: Voice Classification

Practice: College Classification

Key Takeaways

Lesson-end Project: Classify Kinematic Data

Lesson 07 - Unsupervised Learning

Learning Objectives

Overview

Example and Applications of Unsupervised Learning

Clustering

Hierarchical Clustering

Hierarchical Clustering: Example

Load 8 More

Practice: Customer Segmentation

K-means Clustering

Optimal Number of Clusters

Demo: Cluster-Based Incentivization

Practice: Image Segmentation

Key Takeaways

Lesson-end Project: Clustering Image Data

Lesson 08 - Time Series Modeling

Learning Objectives

Overview of Time Series Modeling

Time Series Pattern Types Part A

Time Series Pattern Types Part B

White Noise

Stationarity

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Demo: Air Passengers I

Practice: Beer Production I

Time Series Models Part A

Time Series Models Part B

Time Series Models Part C

Steps in Time Series Forecasting

Demo: Air Passengers II

Practice: Beer Production II

Key Takeaways

Lesson-end Project: IMF Commodity Price Forecast

Lesson 09 - Ensemble Learning

Learning Objectives

Overview

Ensemble Learning Methods Part A

Ensemble Learning Methods Part B

Working of AdaBoost

AdaBoost Algorithm and Flowchart

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XGBoost

XGBoost Parameters Part A

XGBoost Parameters Part B

Demo: Pima Indians Diabetes

Practice: Linearly Separable Species

Model Selection

Common Splitting Strategies

Demo: Cross-Validation

Practice: Model Selection

Key Takeaways

Lesson-end Project: Tuning Classifier Model with XGBoost

Lesson 10 - Recommender Systems

Learning Objectives

Introduction

Purposes of Recommender Systems

Paradigms of Recommender Systems

Collaborative Filtering Part A

Collaborative Filtering Part B

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Association Rule Mining: Market Basket Analysis

Association Rule Generation: Apriori Algorithm

Apriori Algorithm Example: Part A

Apriori Algorithm Example: Part B

Apriori Algorithm: Rule Selection

Demo: User-Movie Recommendation Model

Practice: Movie-Movie recommendation

Key Takeaways

Lesson-end Project: Book Rental Recommendation

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Program Features

Four industry-based course-end projects

Interactive learning with Jupyter notebooks integrated labs

Dedicated mentoring session from faculty of industry experts