Eligibility

The Machine Learning certification course is well-suited for participants at the intermediate level including, Analytics Managers, Business Analysts, Information Architects, Developers looking to become Machine Learning Engineers or Data Scientists, and graduates seeking a career in Data Science and Machine Learning.

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

ain 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

85 percent completion of online self-paced learning or attendance of one live virtual classroom A score of at least 75 percent in the course-end assessment

Successful evaluation in at least one project

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 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

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 Seaborn

• 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 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 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) First Principal Component 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 Decision Tree: Examples 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 Demo: Clustering Animals 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

• Removal of Non-Stationarity 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 Gradient Boosting

• 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 Association Rule Mining

• 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

Lesson 11 - Text Mining

• Learning Objectives Overview of Text Mining Significance of Text Mining Applications of Text Mining

• Natural Language Toolkit Library

• Text Extraction and Preprocessing: Tokenization Text Extraction and Preprocessing: N-grams

• Text Extraction and Preprocessing: Stop Word Removal Text Extraction and Preprocessing: Stemming

• Text Extraction and Preprocessing: Lemmatization Text Extraction and Preprocessing: POS Tagging

• Text Extraction and Preprocessing: Named Entity Recognition NLP Process Workflow

• Demo: Processing Brown Corpus Practice: Wiki Corpus Structuring Sentences: Syntax Rendering Syntax Trees

• Structuring Sentences: Chunking and Chunk Parsing NP and VP Chunk and Parser

• Structuring Sentences: Chinking Context-Free Grammar (CFG) Demo: Twitter Sentiments Practice: Airline Sentiment

• Key Takeaways

• Lesson-end Project: FIFA World Cup