Machine Learning

Machine Learning

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Description

Machine Learning Training at Massive Tech- In this course, you can explore the concepts of Machine Learning and understand how it’s transforming the digital world. An exciting branch of Artificial Intelligence, this Machine Learning course in Bangalore will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.

This Machine Learning online Training offers a deep overview of Machine Learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification and time series modelling.
This Machine learning Training is well-suited for Candidates at the intermediate level including, analytics manager, business analytics, information architects, developers looking to become data scientists and graduates seeking a career in Data Science and Machine Learning.

Prerequisites

Prerequisites

  • Basics Statistics and mathematics at the college level.
  • Familiarity with Python programming is an added advantage.
  • Graduates seeking a career in Data Science and Machine Learning.
Key Features
  • LIVE Instructor-led Classes
  • 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
  • Flexibility to attend the class at your convenient time.
  • Server Access to Massive's Tech Management System until you get into your dream carrier.
  • A huge database of Interview Questions
  • Professional Resume Preparation
  • Earn a Skill Certificate
  • Enroll today and get the advantage.
Curriculum
  • Learning objectives
  • Emergence of Artificial Intelligence
  • Artificial Intelligence in Practice
  • Sci-Fi Movies with the Concepts of AI
  • Recommender Systems
  • Relationship between Artificial Intelligence and Machine Learning
  • Definition and Features of Machine Learning
  • Machine Learning Approaches
  • Machine Learning Techniques
  • Objectives
  • Data Exploration Loading Files
  • Importing and Storing Data
  • Automobile Data Exploration
  • Data Exploration Technique
  • Seaborn
  • Correlation Analysis
  • Automobile Data Exploration
  • Data Wrangling
  • Missing Values in a Dataset
  • Outlier Values in a Dataset
  • Outlier and Missing Value Treatment
  • Data Exploration- C
  • Data Manipulation
  • Functionalities of Data object in Python
  • Different Types of Joins
  • Typecasting
  • Labor Hours Comparison
  • Data Manipulation
  • Objectives
  • 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 Functions
  • Evaluating Coefficients
  • Linear Regression
  • Challenges in Prediction
  • Types of Regression Algorithm
  • Bigmart
  • Sigmoid Probability
  • Accuracy Matrix
  • Survival of Titanic Passengers
  • Iris Species
  • Objectives
  • Feature Selection
  • Regression
  • Factor Analysis
  • Factor Analysis Process
  • Principal Components Analysis (PCA)
  • First Principal Components
  • Eigenvalues and PCA
  • Feature Reduction
  • PCA Transformation
  • Linear Discriminant Analysis
  • Maximum Separable Line
  • Labeled Feature Reduction
  • LDA Transformation
  • Objectives
  • Overview of Classification
  • 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: Cost Matrix
  • Horse Survival
  • Naïve 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
  • Objective & Overview
  • Examples and Applications of Unsupervised Learning
  • Clustering
  • Hierarchical Clustering & Examples
  • Clustering Animals
  • K-means Clustering
  • Optimal Number of Clusters
  • Cluster based Incentivization
  • Image Segmentation
  • Objectives
  • Overview of Time Series Modeling
  • Time Series Pattern
  • White Noise
  • Stationarity
  • Removal of Non-Stationarity
  • Air Passengers – A
  • Beer Production – A
  • Time Series Models
  • Steps in Time Series Forecasting
  • Ensemble Learning
  • Overview
  • Ensemble Learning Methods
  • Working of AdaBoost
  • AdaBoost Algorithm and Flowchart
  • Gradient Boosting
  • XGBoost
  • XGBoost Parameters
  • Model Selection
  • Common Splitting Strategies
  • Tuning Classifier Model with XGBoost
  • Objectives
  • Introduction
  • Purpose of Recommender Systems
  • Paradigms of Recommender Systems
  • Collaborative Filtering
  • Association Rule Mining
  • Association Rule Generation: Apriori Algorithm
  • User Movie Recommendation Model
  • Book Rental Recommendation
  • Objectives
  • Overview of Text Mining
  • Significance of Text Mining
  • Application 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: Names Entity Recognition
  • NLP Process Workflow
  • Structuring Sentences: Syntax
  • Rendering Syntax Trees
  • Structuring Sentence: Chunking and Chunk Parsing
  • NP and VP Chunk and Parser
  • Structuring Sentence: Chinking
  • Context- Free Grammar (CFG)

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