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Course Content
Course Overview
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Course Introduction
02:23
Course Files
Section 1: Introduction to Machine Learning
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Lesson 1. AI and Machine Learning
13:28
Lesson 2. Types of ML – Supervised ML
14:32
Lesson 3. Unsupervised Machine Learning
10:28
Lesson 4. Reinforcement Learning
06:59
Lesson 5. Model Development Steps in ML
14:29
Section 2: Supervised ML- Regression Algorithms
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Lesson 1. linear regression Algorithms
17:34
Lesson 2. Finding the best fit line
09:57
Lesson 3. Model Evaluation and Metrics
11:32
Lesson 4. Project Implementation- Domain Analysis
25:49
Lesson 5. Data Preprocessing and Feature Engineering
14:06
Lesson 6. Feature Selection
25:28
Section 3: Supervised ML- Logistic Regression
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Lesson 1. Introduction Supervised Machine Learning.mp4
08:53
Lesson 2. Logistic Regression
11:10
Lesson 3. Evaluation of a Classification Model
12:09
Lesson 4. Implementation of Python Program
16:53
Lesson 5. Data preprocessing
17:05
Lesson 6. Feature Engineering
12:35
Section 4: Supervised ML- K-Nearest Neighbors Algorithm
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Lesson 1. What is the K-Nearest Neighbors Algorithm?
10:40
Lesson 2. Euclidean Distance
10:22
Lesson 3. Lazy Learners
11:19
Lesson 4. Python implementation
14:06
Lesson 5. Balancing the data-SMOTE
Section 5: Decision Tree and SVM in Machine Learning
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Lesson 1. Decision Tree in Machine Learning
12:06
Lesson 2. Attribute Selection Measures
12:06
Lesson 3. Support Vector Machine (SVM) Algorithm; Business Case
15:23
Lesson 4. Data Preprocessing and project Implementation
15:24
Section 6: AI Chatbot for Interactive Learning and Assistance
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Lesson 1. Introduction to chatbot
03:17
Lesson 2. Setup and Design Preparation
05:05
Lesson 3. Data Preparation and Implementation
13:35
Lesson 4. Integration and Testing
01:06:00
Data Science: Supervised Machine Learning with Python
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