Machine Learning Courses Live Class

4.9 out of 5 (178 Ratings)

Advance your career in Artificial intelligence by our machine learning program to gain in-depth knowledge on all the concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, etc and get equipped with the skills and master the concept of machine learning and step ahead in the digital world as an machine learning engineer.

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Machine Learning Training Overview

Machine Learning Training 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

Machine Learning Training Course Syllabus

Introduction to AI and Machine Learning

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 Preprocessing

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

Supervised Learning

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

Feature Engineering

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

Supervised Learning Classification

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

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

Time Series Modeling

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

Ensemble Learning

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

Recommender Systems

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

Text Mining

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

Projects

Uber Fare Prediction
Amazon - Employee Access
California Housing Price Prediction
Phishing Detector with LR

Machine Learning Training - Tutorials & Learning Guide

Welcome to the complete learning tutorials for Machine Learning Training. These tutorials are designed in simple language with examples, practice questions, and real-world projects to help you master the course step by step.

📘 Course Tutorials

📖 Machine Learning Training Tutorials & Learning

Understand the basics, scope, and career opportunities in Machine Learning Training. Learn why this technology is important and how it is used in real industries.

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🎯 Interview Preparation

Most asked interview questions and real-world scenarios from Machine Learning Training to help you crack interviews.

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Course Features

Sessions 20
Assignment Yes
Duration 2 hours / Class
Language English / Hindi

What People Say

Nagmani Solanki

Digital Marketing

Edugators platform is the best place to learn live classes, and live projects by which you can understand easily and have excellent customer service.

Saurabh Arya

Full Stack Developer

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.

Praveen Madhukar

Web Design

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.