Machine Learning Using Python Course For Beginners

The machine learning using Python course is good for beginners who want to learn machine learning. This course discusses how to use the numpy, pandas, seaborn, matplotlib, sklearn Python libraries for perforing various machine learning tasks like data preprocessing, data visualization, feature selection, model selection, model evaluation, etc. The course also discussed how to solve classification, regression or clustering problems using the sklearn Python library.

Section 1: Introduction To NumPy

Details of Section 1

1.1 What is the NumPy Python library?
1.2 What are NumPy arrays?
1.3 How to print the shape of a NumPy array?
1.4 How to access NumPy array elements?
1.5 How to reshape a NumPy array?
1.6 How to create a NumPy array using linspace in Python?
1.7 How to join two or more NumPy arrays?
1.8 How to load data from a text file using numpy.loadtxt?
1.9 How to load data from a text file using numpy.genfromtxt?
1.10 How to convert a pandas Series and DataFrame to a NumPy array?

Section 2: Introduction To Python Pandas

Details of Section 2

Section 3: Data Visualization using Python Pandas

Details of Section 3

Section 4: Data Visualization Using Python Matplotlib

Details of Section 4

4.1 What is the matplotlib Python library?
4.2 How to plot a line plot using the matplotlib Python library?
4.3 How to plot a scatter plot using the matplotlib Python library?
4.4 How to plot a bar chart using the matplotlib Python library?
4.5 How to plot a pie chart using the matplotlib Python library?
4.6 How to plot a histogram using the matplotlib Python library?
4.7 How to draw the box and whisker plot using the matplotlib Python library?

Section 5: Data Visualization Using Python Seaborn

Details of Section 5

Section 6: Data Preprocessing Using Sklearn

Details of Section 6

6.1 What are estimators, transformers and predictors in sklearn?
6.2 How to perform mean or median imputation using the sklearn Python library?
6.3 How to perform label encoding using sklearn?
6.4 How to perform ordinal encoding using sklearn?
6.5 How to perform One-Hot Encoding using sklearn?
6.6 How to use Column Transformer in sklearn?
6.7 How to perform min-max scaling using sklearn?
6.8 How to perform Maximum Absolute Scaling using sklearn?
7.9 How to use Robust Scaler in sklearn?
6.10 Vector Unit Length Scaling using sklearn
6.11 How to standardize data using sklearn?
6.12 Normalization using sklearn
6.13 Binarization using sklearn

Section 7: Feature Selection Using Sklearn

Details of Section 7

7.1 Feature Selection Based on Variance in Machine Learning
7.2 Feature Selection Based On Correlation In Machine Learning
7.3 Recursive Feature Elimination (RFE) using sklearn
7.4 Feature selection based on model performance using sklearn
7.5 Select K best features using SelectKBest in sklearn
7.6 Feature Importances using Extra Trees in sklearn

Section 8: Model Selection Using Sklearn

Details of Section 8

8.1 What is Train Test Split and how to do it using the sklearn Python library?
8.2 Repeated Random Train-Test Split using sklearn in Python
8.3 K-Fold Cross-Validation using sklearn in Python
8.4 Repeated K-Fold Cross-Validation using Python sklearn
8.5 Repeated Stratified K-Fold Cross-Validation using sklearn in Python
8.6 Leave One Out Cross Validation using sklearn in Python
8.7 Stratified K-Fold Cross-Validation using sklearn in Python

Section 9: Model Evaluation Using Sklearn

Details of Section 9

9.1 Calculating Mean Absolute Error, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) using Python
9.2 What is R-squared and how to calculate it in sklearn?
9.3 Calculating Confusion Matrix using Python
9.4 Calculating Accuracy Score in Machine Learning using Python
9.5 Calculating Precision and Recall in Machine Learning using Python
9.6 Calculating F1 score in machine learning using Python
9.7 Calculate The Logistic Loss Score using sklearn in Python
9.8 Calculate AUC: Area Under The ROC Curve Using sklearn in Python
9.9 How to calculate the classification report using sklearn in Python?

Section 10: Solving Regression Problems Using Python Sklearn

Details of Section 10

10.1 Simple Linear Regression using Python
10.2 Multiple Linear Regression using Python
10.3 Polynomial Regression using Python
10.4 How to use pipeline for polynomial regression in sklearn?
10.5 Ridge Regression using Python
10.6 Lasso Regression using Python
10.7 ElasticNet Regression using Python
10.8 K-Nearest Neighbors (KNN) Regressor using sklearn
10.9 Regression Trees using the sklearn Python library
10.10 Random Forest Regressor using sklearn
10.11 How to solve regression problems using Support Vector Machines (SVM) in sklearn?
10.12 Extra Trees Regressor using sklearn

Section 11: Solving Classification Problems Using Python Sklearn

Details of Section 11

11.1 Logistic Regression using the sklearn Python library
11.2 K-Nearest Neighbors (KNN) Classifier using sklearn
11.3 Linear Discriminant Analysis using sklearn in Python
11.4 Classification Trees using sklearn
11.5 How to plot Decision Trees using sklearn?
11.6 Random Forest Classifier using sklearn in Python
11.7 Gaussian Naive Bayes Classifier using sklearn
11.8 How to solve classification problems using Support Vector Machines (SVM) in sklearn?
11.9 Bagged Decision Trees Classifier using sklearn in Python
11.10 AdaBoost Classifier using sklearn in Python
11.11 Gradient Boosting Classifier using sklearn in Python
11.12 Compare the performance of different machine learning models
11.13 Use pipeline for data preparation and modeling in sklearn

Section 12: Solving Clustering Problems Using Python Sklearn

Details of Section 12

12.1 What is K-Means Clustering and how does it work?
12.2 K-Means Clustering using sklearn
12.3 How to find out the optimal value of k in K-Means Clustering?
12.4 How to measure clustering performance using silhouette score?
12.5 How to measure clustering performance using completeness score?
12.6 How to measure clustering performance using homogeneity score?
12.7 How to measure clustering performance using V-measure?
12.8 How to measure clustering performance using contingency matrix?

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