Machine Learnings

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