Machine Learning Course

Machine Learning

Join the webinar on Machine Learning Course

27-Apr-2024 Register Now

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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

Introduction To AI and Machine Learning

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

Data Preprocessing

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

Supervised Learning

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

Feature Engineering

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

Supervised Learning Classification

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

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

Time Series Modeling

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

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

Recomender System

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

Certifications & Accreditations

Benefits of learning from us

Program Fees

Machine Learning Course

Live Instructor Led Training Fee

₹ 175,000.00

The above fees are applicable to candidates in India only.

Mode of Training

OnDemand

Live Instructor Led

Virtual Lab

Classroom

Comprehensive Curriculum

300+ hours

Learning Content + Practicals

Regular Batch

Date

22-Apr-2024

Time

10:30 AM IST

Fast Track Batch

Date

24-Apr-2024

Time

10:30 AM IST

Extra 5% off on Courses

Coupon Code: UPGRADE

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FAQ For Machine Learning

Does SMEClabs provides placement?

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

What are the different types of Machine learning algorithms?

Supervised learning, Unsupervised learning, and Reinforcement learning.

What is Machine learning?

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

What are the Applications of Machine Learning?

Fraud detection
Image recognition
Medical diagnosis
Web search results

Is there any future in Machine learning?

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.

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