Machine Learning

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?

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

More Info