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Overview

Course Description

Learn all the essentials of machine leaning concepts of developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling and use of python to predict data. Learn to teach computers to act without explicitly programmed. Our life is witnessing an era of transformation of machines from self-driven cars, practical speech recognition, and use of interactive machines all around globe. It is still developing fast and machines are getting smarter with use of artificial intelligence. Learn the effective machine and implementations with Edugators.

Who should go for this training?

Edugators Machine Learning Certification Tarining 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.

Requirements

  • Computer or laptop or Smartphone with Highspeed Internet Connection

  • Understanding of basic statistics and mathematics at the college level

  • Familiarity with Python programming is also beneficial

  • Understanding of Math Refresher and Statistics Essential for Data Science

Course Syllabus

  • Emergence of Artificial Intelligence
  • Sci-Fi Movies with the Concept of AI
  • Recommender Systems
  • Relationship between Artificial Intelligence, Machine Learning, and Data Science
  • Definition and Features of Machine Learning
  • Machine Learning Approaches
  • Machine Learning Techniques
  • Applications of Machine Learning
  • Data Exploration Loading Files
  • Importing and Storing Data
  • Automobile Data Exploration
  • Data Exploration Techniques
  • Seaborn
  • Correlation Analysis
  • Data Wrangling
  • Missing Values in a Dataset
  • Outlier Values in a Dataset
  • Outlier and Missing Value Treatment
  • Data Manipulation
  • Functionalities of Data Object in Python
  • Different Types of Joins
  • Typecasting
  • Labor Hours Comparison
  • Question-Answer Session
  • Introduction to Supervised Learning
  • Supervised Learning- Real-Life Scenario
  • Understanding the Algorithm
  • Supervised Learning Flow
  • Types of Supervised Learning
  • Types of Classification Algorithms
  • Types of Regression Algorithms
  • Regression Use Case
  • Accuracy Metrics
  • Cost Function
  • Evaluating Coefficients
  • Linear Regression
  • Challenges in Prediction
  • Logistic Regression
  • Sigmoid Probability
  • Accuracy Matrix
  • Survival of Titanic Passengers
  • Practice: Iris Species
  • Question-Answer Session
  • Introduction to Feature Selection
  • Regression
  • Factor Analysis
  • Factor Analysis Process
  • Principal Component Analysis (PCA)
  • First Principal Component
  • Eigenvalues and PCA
  • Feature Reduction
  • PCA Transformation
  • Linear Discriminant Analysis
  • Maximum Separable Line
  • Find Maximum Separable Line
  • Labeled Feature Reduction
  • LDA Transformation
  • Question-Answer Session
  • Overview of Classification
  • Classification: A Supervised Learning Algorithm
  • Use Cases of Classification
  • 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 and Cost Matrix
  • Demo: Horse Survival
  • Practice: Loan Risk Analysis
  • Naive Bayes Classifier
  • Steps to Calculate Posterior Probability
  • 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
  • Classify Kinematic Data
  • Question-Answer Session
  • Unsupervised Learning Overview
  • Example and Applications of Unsupervised Learning
  • Clustering
  • Hierarchical Clustering
  • Hierarchical Clustering Example
  • Demo: Clustering Animals
  • Customer Segmentation
  • K-means Clustering
  • Optimal Number of Clusters
  • Demo: Cluster Based Incentivization
  • Practice: Image Segmentation
  • Clustering Image Data
  • Overview of Time Series Modeling
  • Time Series Pattern Types
  • White Noise
  • Stationarity
  • Removal of Non-Stationarity
  • Time Series Models
  • Steps in Time Series Forecasting
  • IMF Commodity Price Forecast
  • Question-Answer Session
  • Introduction to Ensemble Learning
  • Ensemble Learning Methods
  • Working of AdaBoost
  • AdaBoost Algorithm and Flowchart
  • Gradient Boosting
  • XGBoost
  • XGBoost Parameters
  • Demo: Pima Indians Diabetes
  • Practice: Linearly Separable Species
  • Model Selection
  • Common Splitting Strategies
  • Cross Validation
  • Tuning Classifier Model with XGBoost
  • Introduction
  • Purposes of Recommender Systems
  • Paradigms of Recommender Systems
  • Collaborative Filtering
  • Association Rule Mining
  • Association Rule Mining: Market Basket Analysis
  • Association Rule Generation: Apriori Algorithm
  • Apriori Algorithm Example
  • Apriori Algorithm: Rule Selection
  • User-Movie Recommendation Model
  • Book Rental Recommendation
  • Question-Answer Session
  • 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
  • Processing Brown 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)
  • Question-Answer Session
  • Uber Fare Prediction
  • Amazon - Employee Access
  • California Housing Price Prediction
  • Phishing Detector with LR

What People Say

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It was a very good experience. Edugators and the instructor worked with us through the whole process to ensure we received the best training solution for our needs.

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I would definitely recommend taking courses from Edugators. The instructors are very knowledgeable, receptive to questions and willing to go out of the way to help you.

Shyam Kumar

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The instructor's training style was smooth and easy. Individual attention was paid to students and the details provided were very helpful.

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