Machine Learning with Data Science using Python

Course Features
- Lectures 234
- Quizzes 0
- Students 798
- Assessments Yes
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Course Overview
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Introduction to Artificial Intelligence
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Anaconda Installation
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Basics of Python: Oops
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Exploratory: Descriptive Analysis
- Introduction to Data Science
- Basic Terminology of Data Science: Key Term -1
- Basic Terminology of Data Science: Key Term -2
- Types of Variable
- Concepts under Data Science
- Topics Under Descriptive Analysis
- Data Science: Project Report
- What is outlier?
- What is Mean and Purpose of it
- What is Median and Purpose of it Copy
- Mean vs Median Copy
- Mode Copy
- Python: Dataset Explanation Copy
- Python: Mean, Median and Mode Copy
- Python: Quantitative and Qualitative Copy
- Python Class and Function Fair copy Copy
- Python Custom Table for Mean, Median and Mode Copy
- Percentile and Purpose of it Copy
- Python: Percentile and Lesser and greater range of outlier Copy
- Inter Quartile Range and Outlier
- Python IQR
- Python IQR: Lesser range outlier and Greater range outlier
- Python Fair Descriptive
- Python Detecting Lesser and Greater outlier
- Python Detecting Outlier Fair copy
- Cross Checking for replaced outlier
- Frequency , Relative Frequency and Cumulative Frequency
- Python: Frequency, Relative Frequency and Cumulative Frequency Copy
- Fair Frequency, Relative Frequency Copy
- Variance Copy
- Standard Deviation
- Python Standard Variance Copy
- Skewness and Kurtosis Copy
- Python Skewness, Kurt and Histogram Copy
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Exploratory: Inferential Analysis
- What is Inference Analysis?
- Univariate ,Bivariate and Multivariate
- Concept Under each Analysis
- Co-Variance
- Correlation
- Python Covariance, Correlation
- Multicollinearity
- Variance Inflation Factor and Homoscedasticity
- Homoscedasticity and Heteroscedasticity
- Introduction univariate
- Probability Density Curve
- Python PDF
- Python CDF
- Normal Distribution
- Z-Score C
- Python Z-Score
- Types of Test
- Paired & Unpaired T-Test
- Python T Test Copy
- F Test Copy
- Hypothesis Testing Copy
- Analysis of Variance and One way Testing Copy
- Python Anavo One Way Testing Copy
- Anavo Two Analysis Copy
- Python Anavo Two way Testing Copy
- Python Two Post hoc
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Data Visualization: Matplotlib
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Data Visualization: Seaborn
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Data Visualization: Tableau
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Data Preprocessing
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Introduction to Machine Learning-Must Watch
- Topics Under Machine Learning
- Demo of Chronic Kidney Disease
- Types of problem Statement
- Problem identification of Supervised Learning
- Problem identification of Unsupervised Learning
- Problem identification of Semisupervised Learning
- Problem Identification of Regression and Classification
- Algorithm Segregation
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Regression: Supervised
- What is Simple Linear Regression
- Simple Linear Problem Identification
- Simple Linear Regression- Weight and Bais
- Simple Linear Regression -Model & Bestfitline
- Simple Linear Regression-Validation-SSE
- Simple Linear regression-Error
- Simple Linear Regression-Validation-SSR-SST
- Simple Linear Regression-R-Square
- Simple Linear Regression-Adjusted R-Square
- The purpose of training and test set
- Assumption for linear Regression
- Steps to be followed for Machine Learning model.
- Python-SLR-before the model
- Python SLR Model Bulid
- Python SLR Prediction
- Python SLR after the Model
- Multiple Linear Regression
- Python Multiple Linear before the model
- Python Multiple Linear After the Model
- Polynomial Regression
- Problem Statement for Non Linear Algorithm
- Types of Fitting
- Python Polynomial
- Support Vector Machine for Linear
- Support Vector Machine for Non-Linear
- Python Support Vector Regression
- Decision Tree Entropy
- Decision Tree Information Gain
- Python Decision Tree
- Random Forest
- Python Random Forest
- Lasso, Ridge and Elastic
- Python Lasso, Ridge and Elastic
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Classification: Supervised
- Introduction to classification
- Demo for Classification Statement
- Difference between Regression and Classification
- Confusion Matrix
- Confusion Matrix_type1 & Tpye 2 Error
- Confusion Accuracy
- Classification Algorithm
- Logistic Algorithm
- K-Nearest Neighbour
- KNN-2
- Navie Bayes-1
- Navie Bayes-2
- Python Logistic
- Python Logistic-2
- Python SVM_Linear
- Python SVM Nonlinear
- Python Knn
- Python Navie Baye
- Python Decision Tree
- Python Random Forest
- Python All in one Algorithm
- Python Simplified
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Clustering: Unsupervised
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Feature Selection
- Why Feature Selection
- Feature Selection Vs Dimensionality Reduction
- Example for Feature Selection and Dimensionality Reduction
- Algorithm
- SelectK
- Recurssive Feature Elimination
- Feature Importance
- Blueprint for SelectKbest
- Python Selectkbest-1
- Python SelectKbest-2
- Python Selectkbest -Classification 3
- Python Selectkbest -Classification 4
- Python Selectkbest -Classification 5
- Python Selectkbest -Regression
- Python Selectkbest -Regression2
- Python RFE -Classification-1
- Python RFE -Classification-2
- Python RFE-Regression-1
- Python RFE-Regression2
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Dimensionality Reduction
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End to End Project: Classification
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End to End Project: Regression
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Web Development
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Soundarya S
Soundarya
Very easy to understand and grasp
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Arunachalam PL
Very informative and useful course
The course was very useful and got to know many new terms in the concept
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KARTHICKKUMAR M
Informative course
It was informative and thought provoking course
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Keshav Kumar P
Really well compiled course
The course was really easy grasp the basics and importance of DS and ML