Machine Learning Certification Course

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Learners

  • Avail ExcelR's Exclusive JUMBO PASS (Limited Period Offer)

  • Top-Notch Faculty with Rich Industry Experience

  • 60+ Assignments and 2 Real-Time Projects

  • Assured Post-Training Support and Guidance

  • Life Time access to Self-Paced Learning

Price

₹40000

Duration

24 Week

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Machine Learning Certification Course

Overview of

Ensure career success with this Machine Learning course. Learn this exciting branch of Artificial Intelligence with a program featuring 58 hrs of Applied Learning, interactive labs, 4 hands-on projects, and mentoring. With our Machine Learning training, master Machine Learning concepts are required for a Machine learning certification. This Machine Learning online course will provide you with the skills needed to become a successful Machine Learning Engineer today.

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Key Features 

Machine Learning Certification Course

  • Gain expertise with 25+ hands-on exercises

  • 4 real-life industry projects with integrated labs

  • Dedicated mentoring sessions from industry experts

  • 58 hours of Applied Learning

Contact us: +918586014284

Benefits

Machine Learning Training Key Features

100% Money Back Guarantee

  • Gain expertise with 25+ hands-on exercises

  • 4 real-life industry projects with integrated labs

  • Dedicated mentoring sessions from industry experts

  • 58 hours of Applied Learning

Skills Covered

  • Supervised and unsupervised learning

  • Time series modeling

  • Linear and logistic regression

  • Kernel SVM

  • KMeans clustering

  • Naive Bayes

  • Decision tree

  • Random forest classifiers

  • Boosting and Bagging techniques

  • Deep Learning fundamentals


Contact us on WhatsApp: +918586014284

 
 

Machine Learning Certification Course

Curriculum - 

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

Why CertifyMind for this Course:

 
Machine Learning Certification Course

Upon successful completion of the this course, CertifyMind will provide you with an industry-recognized course completion certificate which has lifelong validity.

 

Why Online Bootcamp

Develop skills for real career growth

 

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn from experts active in their field, not out-of-touch trainers

Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.

Learn by working on real-world problems

 

Capstone projects involving real world data sets with virtual labs for hands-on learning

Structured guidance ensuring learning never stops

 

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

CONTACT

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Mobile No: +91-8586014284

email: info@certifymind.com

Office Address: M-64, Saurabh Vihar, Jaitpur, Badarpur, Delhi , India- 110044

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