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What you would learn in Machine Learning course?

"Machine Learning" course "Machine Learning" course is an intermediate-level course designed specifically for beginners and professionals. It covers fundamentals and advanced levels of concepts. The course includes content-based videos and practical demonstrations that demonstrate and explain every step needed for completing the task.

Learning Goals:

When you finish the course, you'll be able to understand:

• Evolution of Artificial Intelligence

• Sci-Fi Movies with the Concept of AI

• Recommender Systems

• Connection Between Artificial Intelligence, Machine Learning Data Science, and Data Science

• Definition and features of Machine Learning

• Machine Learning Approaches

• Machine Learning Techniques

• Machine Learning Applications Machine Learning

• Information Exploration Loading Files

• Importing and Storing of Data

• Data Exploration Techniques

• Seaborn

• Correlation Analysis

• Data Wrangling

• Missing values in a dataset

• Outlier Values in the Dataset

• Missing Value Treatment and Outlier

• Data Manipulation

• The functions of Data Object in Python

• Different types of joins

• Typecasting

• Labor Hours Comparison

• An Introduction to Supervised Instruction

• An example of supervised Learning.

• Understanding the Algorithm

• Supervised Learning Flow

• The types of supervised Learning

• Classification Types Algorithms

• Different types of Regression Algorithms

• Regression Use Case

• Accuracy Metrics

• Cost Function

• Evaluating Coefficients

• Linear Regression

• The Challenges of Prediction

• Regression Algorithms: Different Types

• Bigmart

• Logistic Regression

• Sigmoid Probability

• Accuracy Matrix

• The Survival of Titanic Passengers

• Features Selection

• Principal Component Analysis (PCA)

• Eigenvalues and PCA

• Linear Discriminant Analysis

• Overview of Classification

• Utilization Cases for Classification

• Classification Algorithms

• Decision Tree Classifier

• Examples of Decision Trees

• Decision Tree Formation

• Selecting the Classifier

• Overfitting Decision Trees

• Random Forest Classifier- Bagging and Bootstrapping

• The Decision Tree, as well as the 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

• Unsupervised Learning: Overview

• Examples and applications of unsupervised Learning

• Introduction to Clustering

• K-means Clustering

• The most optimal number of clusters

• Cluster-Based Incentivization

• Overview of Time Modeling for Series

• The Time Series Patterns Types

• White Noise

• Stationarity

• Removal of Non-Stationarity

• Air Passengers

• Beer Production

• Time Series Models

• Steps to Time Series Forecasting

• An overview of Ensemble Learning

• Ensemble Learning Methods

• AdaBoost's working 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 the recommender system

• Objectives of Recommender Systems

• The Paradigms Recommender Systems

• Collaborative Filtering

• Association Rule Mining

• Association Rule Mining: Market Basket Analysis

• The Association Rules Generation Method: Apriori Algorithm

• Apriori Algorithm Example

• The Apriori Algorithm for Rule Selection

• User-Movie Recommendation Model

• The introduction to text mining

• Need to Text Mining

• The applications of text Mining

• Natural Language ToolKit Library

• The Text Extraction process and the Preprocessing Tokenization

• Text Extraction and Preprocessing N-grams

• Processing and Extraction of Text Stop Word Removal

• Preprocessing and Text Extraction: Stemming

• Processing and Extraction of Text Lemmatization

• Text Extraction and Preprocessing POS Tagging

• Text Extraction and Preprocessing Named Entity Recognition

• NLP Process Workflow

• Wiki Corpus

...and much more!

Course Content:

• Know AI as well as Machine Learning in greater detail
• Know the importance of data processing
• Define Supervised Learning
• Description of the Feature Engineering
• Recognize the classifications of Supervised Learning
• Define Unsupervised Learning
• Know Time Series Modeling
• Explain the concept of Ensemble Learning
• Explain Recommender Systems
• Learn Text Mining

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