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Machine Learning Course

  • Course Code - SLN - 1718
  • Difficulty - Beginner / No experience
  • Eligibility - Any Degree, Diploma, Graduates
  • Mode - Online / Offline / OnDemand / Hybrid
  • Duration - 300 Hours
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Machine Learning

Join the webinar on Machine Learning Course

10-Jun-2023 Register Now

78%

Alumni Career Transitions

5200+

Hiring Partners

60%

Avg Salary Hike

22

Years of R & D in Syllabus

Machine Learning

This online course offers an in-depth overview of machine learning topics, including working withreal-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. You will also learn how to use Python to draw predictions from data.

Program Features:

  • Online/offline learning
  • Instructor-led training
  • Interactive learning with Jupyter notebooks and pycharm integrated labs
  • Dedicated mentoring session from faculty of industry experts

Machine Learning Course - Syllabus

  • Course Introduction
  • Accessing Practice Lab
  • Learning Objectives
  • 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
  • Definition and Features of Machine Learning
  • Machine Learning Approaches
  • Machine Learning Techniques
  • Learning Objectives
  • Data Exploration Loading Files
  • Demo: Importing and Storing Data
  • Practice: Automobile Data Exploration
  • Seaborn
  • Demo: Correlation Analysis
  • Data Wrangling
  • Missing Values in a Dataset
  • Outlier Values in a Dataset
  • Demo: Outlier and Missing Value Treatment
  • Data Manipulation
  • Functionalities of Data Object in Python
  • Different Types of Joins
  • Typecasting
  • Demo: Labor Hours Comparison
  • Practice: Data Manipulation
  • Learning 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 Function
  • Evaluating Coefficients
  • Demo: Linear Regression
  • Practice: Boston Homes
  • Challenges in Prediction
  • Types of Regression Algorithms
  • Practice: Boston Homes
  • Logistic Regression
  • Sigmoid Probability
  • Accuracy Matrix
  • Demo: Survival of Titanic Passengers
  • Practice: Iris Species
  • Learning Objectives
  • 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
  • Demo: Labeled Feature Reduction
  • LDA Transformation
  • Learning Objectives
  • 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
  • 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
  • Support Vector Machines : Linear Separability
  • Support Vector Machines
  • Linear SVM : Mathematical Representation
  • Non-linear SVMs
  • 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
  • Learning Objectives
  • Overview of Time Series Modeling
  • Time Series Pattern Types
  • White Noise
  • Stationarity
  • Removal of Non-Stationarity
  • Demo: Air Passengers
  • Practice: Beer Production
  • Time Series Models
  • Steps in Time Series Forecasting
  • IMF Commodity Price Forecast
  • Ensemble Learning
  • Overview
  • 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
  • Demo: Cross Validation Selection
  • Learning Objectives
  • 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
  • Demo: User-Movie Recommendation Model
  • Practice: Movie-Movie recommendation
  • 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
  • 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: Structuring Sentences
  • Airline Sentiment
  • FIFA World Cup

** The above is the lite syllabus and doesn’t cover the full syllabus. To get full syllabus  Book a Free Demo Now

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Certifications & Accreditations

NSDC 2 - Machine Learning Course
iisc 1 - Machine Learning Course
IASC 1 - Machine Learning Course
TUV 1 - Machine Learning Course
ESSI - Machine Learning Course
CGSC - Machine Learning Course

Benefits of learning from us

  • Designed for Working Professionals
  • Personalised mentorship sessions
  • Dedicated career support
  • Doubt-Solving with Expert Industry mentors
  • Proactive Program Support
  • Job Assistance with Top Firms
  • IIIT Kanpur Alumni organised syllabus
  • Virtual Lab Facility
  • 100% Placement Assistance
  • National & International Certifications

Program Fees

Machine Learning Course

Live Instructor Led Training Fee

₹ 175,000.00
  • The above fees are applicable to candidates in India only.
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Mode of Training

OnDemand

Live Instructor Led

Virtual Lab

Classroom

Comprehensive Curriculum

300+ hours

Learning Content + Practicals

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Regular Batch

Date

05-Jun-2023

Time

10:30 AM IST

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Date

07-Jun-2023

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Related courses of Machine Learning Course

FAQ For Machine Learning

Yes, SMEClabs provides placement you can visit this website Placementshala to get more details regarding the placements.

Supervised learning, Unsupervised learning, and Reinforcement learning.

Machine learning is the process by which computers would be able to learn themselves.

 
  • Fraud detection
  • Image recognition
  • Medical diagnosis
  • Web search results

Machine learning is a part of AI, so in the future, there would be a lot of opportunities and it is a good-paying job.

 
Best Machine Learning Course | Online/Offline Class | SMEClabs
Machine Learning - Machine Learning Course

Best Machine Learning Course is an entry level program. We guide all students from beginner to advanced level so students get opportunity to learn from scratch

Course Provider: Organization

Course Provider Name: SMEClabs

Course Provider URL: https://courses.smeclabs.com/courses/machine-learning/

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