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Machine Learning Certification Course

Course Description

44 hours

Machine Learning Certification Course

Explore the concepts of Machine Learning and understand how it’s transforming the digital world. An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.

Machine Learning Course Overview

This Machine Learning online course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning certification training to draw predictions from data.

Eligibility

The Machine Learning certification online course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

Pre-requisites

This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

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Course Syllabus

Lesson 01 Course Introduction

Course Introduction
Accessing Practice Lab

Lesson 02 Introduction to AI and Machine Learning

2.1 Learning Objectives
2.2 Emergence of Artificial Intelligence
2.3 Artificial Intelligence in Practice
2.4 Sci-Fi Movies with the Concept of AI
2.5 Recommender Systems
2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
2.8 Definition and Features of Machine Learning
2.9 Machine Learning Approaches
2.10 Machine Learning Techniques
2.11 Applications of Machine Learning: Part A
2.12 Applications of Machine Learning: Part B
2.13 Key Takeaways
Knowledge Check

Lesson 03 Data Preprocessing

3.1 Learning Objectives
3.2 Data Exploration Loading Files: Part A
3.2 Data Exploration Loading Files: Part B
3.3 Demo: Importing and Storing Data
Practice: Automobile Data Exploration – A
3.4 Data Exploration Techniques:
Part A
3.5 Data Exploration Techniques: Part B
3.6 Seaborn
3.7 Demo: Correlation Analysis
Practice: Automobile Data Exploration – B
3.8 Data Wrangling
3.9 Missing Values in a Dataset
3.10 Outlier Values in a Dataset
3.11 Demo: Outlier and Missing Value Treatment
Practice: Data Exploration – C
3.12 Data Manipulation
3.13 Functionalities of Data Object in Python: Part A
3.14 Functionalities of Data Object in Python: Part B
3.15 Different Types of Joins
3.16 Typecasting
3.17 Demo: Labor Hours Comparison
Practice: Data Manipulation
3.18 Key Takeaways
Knowledge Check
Storing Test Results

Lesson 04 Supervised Learning

4.1 Learning Objectives
4.2 Supervised Learning
4.3 Supervised Learning- Real-Life Scenario
4.4 Understanding the Algorithm
4.5 Supervised Learning Flow
4.6 Types of Supervised Learning: Part A
4.7 Types of Supervised Learning: Part B
4.8 Types of Classification Algorithms
4.9 Types of Regression Algorithms: Part A
4.10 Regression Use Case
4.11 Accuracy Metrics
4.12 Cost Function
4.13 Evaluating Coefficients
4.14 Demo: Linear Regression
Practice: Boston Homes – A
4.15 Challenges in Prediction
4.16 Types of Regression Algorithms: Part B
4.17 Demo: Bigmart
Practice: Boston Homes – B
4.18 Logistic Regression: Part A
4.19 Logistic Regression: Part B
4.20 Sigmoid Probability
4.21 Accuracy Matrix
4.22 Demo: Survival of Titanic Passengers
Practice: Iris Species
4.23 Key Takeaways
Knowledge Check
Health Insurance Cost

Lesson 05 Feature Engineering

5.1 Learning Objectives
5.2 Feature Selection
5.3 Regression
5.4 Factor Analysis
5.5 Factor Analysis Process
5.6 Principal Component Analysis (PCA)
5.7 First Principal Component
5.8 Eigenvalues and PCA
5.9 Demo: Feature Reduction
Practice: PCA Transformation
5.10 Linear Discriminant Analysis
5.11 Maximum Separable Line
5.12 Find Maximum Separable Line
5.13 Demo: Labeled Feature Reduction
Practice: LDA Transformation
5.14 Key Takeaways
Knowledge Check
Simplifying Cancer Treatment

Lesson 06 Supervised Learning Classification

6.1 Learning Objectives
6.2 Overview of Classification
Classification: A Supervised Learning Algorithm
6.4 Use Cases of Classification
6.5 Classification Algorithms
6.6 Decision Tree Classifier
6.7 Decision Tree Examples
6.8 Decision Tree Formation
6.9 Choosing the Classifier
6.10 Overfitting of Decision Trees
6.11 Random Forest Classifier- Bagging and Bootstrapping
6.12 Decision Tree and Random Forest Classifier
Performance Measures: Confusion Matrix
Performance Measures: Cost Matrix
6.15 Demo: Horse Survival
Practice: Loan Risk Analysis
6.16 Naive Bayes Classifier
6.17 Steps to Calculate Posterior Probability: Part A
6.18 Steps to Calculate Posterior Probability:
Part B
6.19 Support Vector Machines : Linear Separability
6.20 Support Vector Machines : Classification Margin
6.21 Linear SVM : Mathematical Representation
6.22 Non-linear SVMs
6.23 The Kernel Trick
6.24 Demo: Voice Classification
Practice: College Classification
6.25 Key Takeaways
Knowledge Check
Classify Kinematic Data

Lesson 07 Unsupervised Learning

7.1 Learning Objectives
7.2 Overview
7.3 Example and Applications of Unsupervised Learning
7.4 Clustering
7.5 Hierarchical Clustering
7.6 Hierarchical Clustering Example
7.7 Demo: Clustering Animals
Practice: Customer Segmentation
7.8 K-means Clustering
7.9 Optimal Number of Clusters
7.10 Demo: Cluster Based Incentivization
Practice: Image Segmentation
7.11 Key Takeaways
Knowledge Check
Clustering Image Data

Lesson 08 Time Series Modeling

8.1 Learning Objectives
8.2 Overview of Time Series Modeling
8.3 Time Series Pattern Types: Part A
8.4 Time Series Pattern Types: Part B
8.5 White Noise
8.6 Stationarity
8.7 Removal of Non-Stationarity
8.8 Demo: Air Passengers – A
Practice: Beer Production – A
8.9 Time Series Models: Part A
8.10 Time Series Models: Part B
8.11 Time Series Models: Part C
8.12 Steps in Time Series Forecasting
8.13 Demo: Air Passengers – B
Practice: Beer Production – B
8.14 Key Takeaways
Knowledge Check
IMF Commodity Price Forecast

Lesson 09 Ensemble Learning

9.01 Ensemble Learning
9.2 Overview
9.3 Ensemble Learning Methods: Part A
9.4 Ensemble Learning Methods: Part B
9.5 Working of AdaBoost
9.6 AdaBoost Algorithm and Flowchart
9.7 Gradient Boosting
9.8 XGBoost
9.9 XGBoost Parameters: Part A
9.10 XGBoost Parameters: Part B
9.11 Demo: Pima Indians Diabetes
Practice: Linearly Separable Species
9.12 Model Selection
9.13 Common Splitting Strategies
9.14 Demo: Cross Validation
Practice: Model Selection
9.15 Key Takeaways
Knowledge Check
Tuning Classifier Model with XGBoost

Lesson 10 Recommender Systems

10.1 Learning Objectives
10.2 Introduction
10.3 Purposes of Recommender Systems
10.4 Paradigms of Recommender Systems
10.5 Collaborative Filtering: Part A
10.6 Collaborative Filtering: Part B
10.7 Association Rule Mining
Association Rule Mining: Market Basket Analysis
10.9 Association Rule Generation: Apriori Algorithm
10.10 Apriori Algorithm Example: Part A
10.11 Apriori Algorithm Example: Part B
10.12 Apriori Algorithm: Rule Selection
10.13 Demo: User-Movie Recommendation Model
Practice: Movie-Movie recommendation
10.14 Key Takeaways
Knowledge Check
Book Rental Recommendation

Lesson 11 Text Mining

11.1 Learning Objectives
11.2 Overview of Text Mining
11.3 Significance of Text Mining
11.4 Applications of Text Mining
11.5 Natural Language ToolKit Library
11.6 Text Extraction and Preprocessing:
Tokenization
11.7 Text Extraction and Preprocessing: N-grams
11.8 Text Extraction and Preprocessing: Stop Word Removal
11.9 Text Extraction and Preprocessing: Stemming
11.10 Text Extraction and Preprocessing: Lemmatization
11.11 Text Extraction and Preprocessing: POS Tagging
11.12 Text Extraction and Preprocessing: Named Entity Recognition
11.13 NLP Process Workflow
11.14 Demo: Processing Brown Corpus
Wiki Corpus
11.15 Structuring Sentences: Syntax
11.16 Rendering Syntax Trees
11.17 Structuring Sentences: Chunking and Chunk Parsing
11.18 NP and VP Chunk and Parser
11.19 Structuring Sentences: Chinking
11.20 Context-Free Grammar (CFG)
11.21 Demo: Structuring Sentences
Practice: Airline Sentiment
11.22 Key Takeaways
Knowledge Check
FIFA World Cup

Lesson 12 Project Highlights

Project Highlights
Uber Fare Prediction
Amazon – Employee Access

Practice Projects

California Housing Price Prediction
Phishing Detector with LR

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You Will Get Certification After Completetion This Course.

Instructor Led Lectures
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Visual Demonstrations, Educational Games & Flashcards
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Mobile Optimization & Progress Tracking
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Practice Quizzes And Exams
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World Class Learning Management System
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Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.
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