Course Features
- Lectures 56
- Quiz 0
- Duration 16 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Self
- 9 Sections
- 56 Lessons
- 16 Hours
- MODULE 1 - Introduction to Artificial Intelligence and Machine Learning6
- MODULE 2 - Data Structures & Managing Data Using Python9
- 2.0Data and Data Types
- 2.1Deep Dive into Python Programming
- 2.2Variables and Operators in Python Programming
- 2.3Data Vectors and Data Frames
- 2.4Reading and Writing Data Files to Python
- 2.5Communicating with Database via Python
- 2.6Executing SQL Using Python
- 2.7Joining Structured & Semi Structured Data with Python
- 2.8Big Data Concepts & Application of Python
- MODULE 3 - Exploring Data Using Python Programming7
- MODULE 4 - Basic Classification Models and Techniques7
- 4.0Introduction to Classification in Machine Learning and Its Application
- 4.1Supervised vs. Unsupervised Classification
- 4.2Deploying Classification Models Using Gen AI x Python
- 4.3What is Decision Tree Classification?
- 4.4Random Forest Classification
- 4.5Naive Bayes Classificaiton
- 4.6Support Vector Machine (SVM) Classification
- MODULE 5 - Regression Method and Forecasting9
- 5.0What is Regression Modelling?
- 5.1Different Stages of Modelling
- 5.2Deployment of Regression Models using Gen AI x Python
- 5.3Simple Linear Regression
- 5.4Multiple Linear Regression
- 5.5Refining the Model
- 5.6Model Validation and Prediction
- 5.7Logistic Regression
- 5.8Evaluating Logistic Regression using Confusion Matrix
- MODULE 6 - Finding Data Patterns Using Association Rules5
- MODULE 7 - K-Means Clustering iLearning Models4
- MODULE 8 - Evaluating and Improving Model Performance4
- MODULE 9 - Deep Dive into Deep Learning5






