Machine Learning & Data Science in Python: Zero to Mastery
About Course
Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python
What Will You Learn?
- Understanding the basic concepts of Machine Learning
- Complete tutorial about basic packages like NumPy and Pandas
- Data Visualization
- Data Preprocessing
- Understanding the concept behind the algorithms
- Developing different kinds of Machine Learning models
- Knowing how to optimize your models' hyperparameters
- Learn how to develop models based on the requirement of your future business
Course Content
Introduction
-
Basic Concepts and Terms
00:00 -
Python IDE
02:24 -
IDE Installation
02:49 -
Installation of Required Libraries
07:31 -
Spyder IDE Interface
07:00
Machine Learning Useful Packages (Libraries)
-
Draft Lesson
-
NumPy Package Part1
07:24 -
NumPy Package Part2
09:11 -
NumPy Package Part3
12:22 -
NumPy Package Part4
06:32 -
NumPy Package Part5
18:06 -
NumPy Package Part6
16:44 -
Pandas Package Part1
15:39 -
Pandas Package Part2
15:11 -
Pandas Package Part3
14:44 -
Pandas Package Part4
24:19 -
Visualization with Matplotlib Part1
14:56 -
Visualization with Matplotlib Part2
22:13 -
Visualization with Matplotlib Part3
18:53 -
Visualization with Matplotlib Part4
16:02 -
Visualization with Matplotlib Part5
12:56
Data Preprocessing
-
Importing and Modifying a Dataset
20:01 -
Statistics Part1
09:01 -
Statistics Part2
20:06 -
Statistics Part3 – Covariance
13:38 -
Missing Values Part1
13:20 -
Missing Values Part2
20:51 -
Outlier Detection Part1
11:15 -
Outlier Detection Part2
15:05 -
Outlier Detection Part3
02:28 -
Concatenation
07:17 -
Dummy Coding
06:55 -
Normalization
20:15
Introduction to Machine Learning
-
Machine Learning Types
07:20
Supervised Learning – Classification
-
Introduction and Understanding the Data
29:50 -
kNN Concepts
10:06 -
kNN Model Development
14:56 -
Training and Test Sets Creation
24:08 -
Decision Tree (DT) Concepts
06:11 -
Decision Tree Model Development
06:56 -
Cross Validation
08:34 -
Naive Bayes Concepts
14:00 -
Naive Bayes Model Development
06:22 -
Logistic Regression Concepts
03:04 -
Logistic Regression Model Development
11:12 -
Evaluation Metrics – Concepts
17:27 -
Evaluation Metrics – In Python
18:44
Supervised Learning – Regression
-
Note!
-
Simple & Multiple Linear Regression
28:47 -
Multiple Linear Regression – Model Development
08:00 -
Evaluation Metrics – Concepts
11:25 -
Evaluation Metrics – In Python
16:51 -
Polynomial Linear Regression Concepts
05:56 -
Polynomial Linear Regression Model Development
19:41 -
Random Forest Concepts
06:34 -
Random Forest Model Development
22:33 -
Support Vector Regression Concepts
07:07 -
Support Vector Regression Model Development
10:36
Unsupervised Learning – Clustering Techniques
-
Introduction
07:21 -
K-means Concepts1
09:36 -
K-means Concepts2
05:54 -
K-means Model Development1
04:37 -
K-means Model Development2
12:30 -
K-means – Model Evaluation
10:59 -
DBSCAN Concepts
05:31 -
DBSCAN Model Development
09:48 -
Hierarchical Clustering Concepts
05:27 -
Hierarchical Clustering Model Development
15:59
Hyper Parameter Tuning
-
Introduction
04:21 -
Support Vector Regression – Model Tuning
12:56 -
K-Means – Model Tuning
02:15 -
k-NN – Model Tuning
13:21 -
Overfitting and Underfitting
09:58
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