Machine Learning course for Beginners & Professionals

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 perform and explain 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 Learning.
• 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 to Machine Learning.