Machine Learning with Python

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Machine Learning with Python

The main purpose of our course is to make you able to learn Machine Learning with python programming language.

3.3 Rating
(1 Reviews)
1 User Enrolled

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Sk Hapijul Hossen

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Member Since 2021

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What you will learn

Learn programming in Python for Machine Learning from scratch.

Learn how to use the most sought after Python packages.

Learn to clean and prepare data for Machine Learning

Understand the story your data is narrating by summarizing the data, checking its variability and shape.

Course Content

23 sections • 98 lectures • 02h 43m total length

	1.1 Agenda		1.5min
	1.2 History		min
	1.3 What Is Machine Learning		mb
	1.4 Application Of Machine Learning		mb
	1.5 Vision Of Machine Learning		mb
	1.6 Skills Required		mb
	1.7 Scope Of Machine Learning		mb

	2.1 Statistics		15.16min
	2.2 Measure Of Center		16.40min
	2.3 Median		mb
	1.4 Mode		mb
	1.5 Measure Of Spread		mb
	2.6 Range		mb
	2.7 Variance & Standard Deviation		mb

	3.1 Probability - Basics		2.5min
	3.2 Conditional probability		5.57min

	4.1 NumPy - 1		1.12min
	4.2 NumPy - 2		6.4min
	4.3 NumPy - 3		4.25min
	4.4 NumPy - 4		2.49min
	4.5 NumPy - 5		1.34min
	4..6 NumPy - 6		2.0min
	4.7 NumPy - 7		1.38min
	4..8 NumPy - 8		3.33min
	4.9 NumPy - 9		3.25min
	4.10 NumPy - 10		1.54min

	5.1 LinePlot		2.05min
	5.2 BarPLot		1.13min
	5.3 Histogram		0.53min
	5.4 Scatter Plot		2.08min
	5.5 Pie Chart		4.33min
	5.6 3D Plot		min
	5.7 Live PLot		min

	7.1 Linear Regression		3.2min
	7.1 Linear Regression		3.2min
	7.2 Evaluation and optimization		4.3min
	7.3 KFolf CrossValidation		1.5min
	7.4 Preparation		11min
	7.5 Selection, Modeling		12.4min
	7.6 Other Features		1.3min
	7.7 Combination of Feature		1.3min
	7.8 Cross Validation		14.5min
	7.9 Model Visualization		1.3min
	7.10 Model Visualization 2		4.5min

	8.1-Logistic Regression		7.4min
	8.2-Data Preparation for Model		14.2min
	8.3-Evaluation of Classification		5.4min
	8.4-Testing		min
	8.5- Evaluating Model		min
	8.6 Evaluating Model 2		min
	8.7 Cross Validation		min
	8.8 Visualization of Data		min
	8.9 Model Visualization		min

	9.1-KNN-Working		min
	9.2 Data Preparation-Modeling		min
	9.3 Evaluation ParameterTuning		min
	9.4 Training & Evaluation		min
	9.5-KNN Model Visualization		min

	10.1-KNN-Classification		min
	10.2-KNN-DataPreparation		min
	10.3-KNN-Modelling		min
	10.4-KNN-CrossValidation		min
	10.5-Finding Best Parameter		min
	10.6 Training with Best Parameters		min
	10.7-Model Visualization		min

	11.1-Decision Tree Regression		min
	11.2-Data Preparation		min
	11.3-Modelling		min
	11.4-Parameter Optimization		min
	11.5-Cross Validation		min
	11.6-Vizualization		min