Machine Learning by Skillcart E learning

Published 09/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 14 lectures (3h 29m) | Size: 1.24 GB

All about Machine Learning!

What you’ll learn
Understand AI and Machine Learning in detail
Understand Data Preprocessing
Define Supervised Learning
Describe Feature Engineering
Identify the Classifications of Supervised Learning
Define Unsupervised Learning
Understand Time Series Modeling
Describe Ensemble Learning
Explain Recommender Systems
Understand Text Mining

Requirements
No prerequisites are required, as the course covers the concepts from the scratch. However, basic knowledge of Python would help.

Description
About the Course

The “Machine Learning” course is an intermediate level course, curated exclusively for both beginners and professionals. The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task.

Learning Objectives

By the end of the course, you will be able to learn about

Evolution 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

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

Introduction to Supervised Learning

Example of Supervised Learning

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

Types of Regression Algorithms

Bigmart

Logistic Regression

Sigmoid Probability

Accuracy Matrix

Survival of Titanic Passengers

Feature Selection

Principal Component Analysis (PCA)

Eigenvalues and PCA

Linear Discriminant Analysis

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: Confusion Matrix

Performance Measures: Cost Matrix

Naive Bayes Classifier

Support Vector Machines : Linear Separability

Support Vector Machines : Classification Margin

Non-linear SVMs

Overview of unsupervised learning

Example and Applications of Unsupervised Learning

Introduction to Clustering

K-means Clustering

Optimal Number of Clusters

Cluster Based Incentivization

Overview of Time Series Modeling

Time Series Pattern Types

White Noise

Stationarity

Removal of Non-Stationarity

Air Passengers

Beer Production

Time Series Models

Steps in Time Series Forecasting

Overview of Ensemble Learning

Ensemble Learning Methods

Working of AdaBoost

AdaBoost Algorithm and Flowchart

Gradient Boosting

Introduction to XGBoost

Parameters of XGBoost

Pima Indians Diabetes

Model Selection

Common Splitting Strategies

Cross Validation

Introduction to recommender system

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

Introduction to text mining

Need 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

Wiki Corpus

…and much more!

If you’re new to this technology, don’t worry – the course covers the topics from the basics. If you’ve done some programming before, you should pick it up quickly.

If you’re a programmer looking to switch into an exciting new career track, this course will teach you the basic techniques used by real-world industry Machine Learning developers. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

Who this course is for
Python developers curious about Machine Leaning
Candidates who are willing to learn Machine Learning from scratch
Python developers willing to upskill themselves
Data Scientist willing to upskill themselves
IT professional willing to switch their career in Machine Learning


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