Machine Learning Certification Course

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.

What you'll learn

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  • Learn Research
  • Collect Usefull Data
  • Requirement Analysis Phase
  • Market competent Skills
  • Problem Solving Skills
  • Model Implementation Skills
  • Building or Developing Phase
  • Presenting and Testing Skills

    Course Includes:

  • 7+ hours on-demand video
  • 7+ articles
  • 20+ downloadable resources
  • Full access
  • Access on mobile and TV
  • Certificate of completion

Benefits

Whether you work for a small company, a large corporate or from home, a computer will be one of the first pieces of office equipment you’re going to need. And they comes in different forms, such as laptops and desktops. Computer skills are a valuable addition to any employee’s personal portfolio. Upskilling and polishing your computer literacy can greatly increase your desirability to employers. This is the perfect opportunity to take on roles you might not have previously considered. As an employer, motivating your employees to become computer literate will increase productivity and also stave off problems that can cost time and significant amounts of money. Many companies have started to depend upon computerised technology to get work done. Which is why computer skills have become increasingly important. Having the necessary and basic computer course knowledge will put you a step ahead of others. You’ll have a big advantage over those who aren’t computer literate. It’s for this specific reason that many schools and tertiary institutions encourage students to complete basic computer studies. Here are three reasons why being computer literate is beneficial in the workplace.

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SELF-PACED LEARNING

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  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 2 simulation test papers for self-assessment
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  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
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CORPORATE TRAINING

  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support
  • 24x7 learner assistance and support

Machine Learning Course Curriculum

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.

Course Content

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
Practice: 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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IT Nuggets Online is a progressive IT company engaged in creating eye-grabbing computer-based content in English, for the benefit of students. we offer learning process that blends texts, visuals, animation, video clips, and sound to give a complete learning experience to students.
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Yes, You Can Enroll More Than Courses.
After Meeting the Certification Criteria Explained By Instructor which will be contain some quiz and hands on exercise then definitely you will get certification.