Python Machine Learning & Data Science for Beginners: Learn by Coding Course by WACAMLDS

Python Machine Learning & Data Science for Beginners: Learn by Coding Course

End to End Python Machine Learning Recipes & Crash Course in Jupyter Notebook for Beginners and Business Students & Graduates.

What's included?

File Icon 183 files

Contents

All Modules in One Zip File
Python Machine Learning Crash Course - Learn By Coding.zip
10.6 MB
Module 01: Machine Learning Ecosystem
How to check installed version of Matplotlib.ipynb
1.43 KB
How to check installed version of Pandas.ipynb
2.97 KB
How to check installed version of NumPy.ipynb
3.35 KB
How to check installed version of SciPy.ipynb
1.5 KB
How to check installed version of Python.ipynb
2.1 KB
How to check installed version of scikit-learn.ipynb
1.52 KB
How to check installed version of Matplotlib.html
269 KB
How to check installed version of Python.html
271 KB
How to check installed version of scikit-learn.html
269 KB
How to check installed version of SciPy.html
269 KB
How to check installed version of Pandas.html
271 KB
How to check installed version of NumPy.html
272 KB
Module 02: Getting Start with Python
Assignment in Python.ipynb
2.74 KB
Data Structure in Python.ipynb
14.7 KB
Flow-Control in Python.ipynb
19 KB
Assignment in Python.html
273 KB
Data Structure in Python.html
311 KB
Flow-Control in Python.html
314 KB
Module 03: Getting Start with Pandas
Automobile_data_update.csv
3.38 KB
Automobile_data.csv
3.13 KB
Pandas & Python Crash Course.ipynb
48.7 KB
Pandas & Python Crash Course.html
317 KB
Module 04: Getting Start with Numpy
NumPy & Python Crash Course.ipynb
11.9 KB
NumPy & Python Crash Course.html
300 KB
Module 05: Getting Start with Matplotlib
company_sales_data.csv
659 Bytes
Matplotlib & Python Crash Course.ipynb
283 KB
Matplotlib & Python Crash Course.html
589 KB
Module 06: Getting Start with Python Random Numbers
Random Numbers in Python.ipynb
9.17 KB
Random Numbers in Python.html
293 KB
Module 07: Getting Start with Python and RDBMS
Python and RDBMS.ipynb
9.33 KB
Python and RDBMS.html
296 KB
Module 08: Load Data for Machine Learning
pima-indians-diabetes.data.csv
22.7 KB
How to Load Data From a csv using Numpy.ipynb
3.39 KB
How to Load Data From a csv using Pandas.ipynb
4.18 KB
How to Load Data From a csv.ipynb
3.52 KB
How to Load Data From url.ipynb
2.28 KB
How to Load Data From url using Pandas.ipynb
3.34 KB
How to Load Data From a csv using Numpy.html
271 KB
How to Load Data From a csv.html
271 KB
How to Load Data From url using Pandas.html
271 KB
How to Load Data From url.html
271 KB
How to Load Data From a csv using Pandas.html
272 KB
Module 09: Descriptive Statistics - Understand the Data
pima-indians-diabetes.data.csv
22.7 KB
How to get class distribution in Data.ipynb
1.24 KB
How to get correlation coefficient.ipynb
2.17 KB
How to get data types of each feature in Data.ipynb
1.42 KB
How to get dimention of Dataset.ipynb
2.72 KB
How to get SKEW statistics of Dataset.ipynb
1.39 KB
How to get statistics of Dataset.ipynb
2.13 KB
How to get class distribution in Data.html
269 KB
How to get correlation coefficient.html
270 KB
How to get dimention of Dataset.html
271 KB
How to get SKEW statistics of Dataset.html
269 KB
How to get statistics of Dataset.html
270 KB
How to get data types of each feature in Data.html
269 KB
Module 10: Data Visualisation - Understand the Data
pima-indians-diabetes.data.csv
22.7 KB
Boxplots.ipynb
30 KB
histogram plots.ipynb
31.8 KB
Correlation Matrix.ipynb
36.4 KB
density plots.ipynb
84.8 KB
scatter plots.ipynb
524 KB
density plots.html
353 KB
Correlation Matrix.html
287 KB
Boxplots.html
298 KB
histogram plots.html
300 KB
scatter plots.html
792 KB
Module 11: Data Preparation for Machine Learning
pima-indians-diabetes.data.csv
22.7 KB
How to rescale Data.ipynb
2.33 KB
binarization.ipynb
1.96 KB
How to standardize Data.ipynb
2.41 KB
normalization.ipynb
2.32 KB
How to standardize Data.html
271 KB
normalization.html
271 KB
How to rescale Data.html
271 KB
binarization.html
271 KB
Module 12: Feature Selection for Machine Learning
pima-indians-diabetes.data.csv
22.7 KB
Feature Extraction with Univariate Statistics.ipynb
2.31 KB
How to get Feature Importance.ipynb
2.19 KB
How to get important Feature with PCA.ipynb
2.16 KB
Feature Extraction with RFE.ipynb
3.41 KB
How to get Feature Importance.html
271 KB
Feature Extraction with Univariate Statistics.html
272 KB
Feature Extraction with RFE.html
273 KB
How to get important Feature with PCA.html
271 KB
Module 13: Resampling in Machine Learning
pima-indians-diabetes.data.csv
22.7 KB
Cross Validation.ipynb
1.78 KB
How to prepare train test dataset.ipynb
1.79 KB
Shuffle Split Cross Validation.ipynb
1.85 KB
Leave One Out Cross Validation.ipynb
1.72 KB
Cross Validation.html
271 KB
Shuffle Split Cross Validation.html
272 KB
Leave One Out Cross Validation.html
271 KB
How to prepare train test dataset.html
271 KB
Module 14: Performance Metrics for Machine Learning
housing.csv
47.9 KB
pima-indians-diabetes.data.csv
22.7 KB
How to get Classification Accuracy.ipynb
1.77 KB
How to get Classification Confusion Matrix.ipynb
1.89 KB
How to get Classification AUC ROC.ipynb
1.74 KB
How to get Classification LogLoss Metric.ipynb
1.77 KB
How to get Classification Report.ipynb
2.36 KB
How to get Regression MAE.ipynb
1.8 KB
How to get Regression MSE.ipynb
1.84 KB
How to get Regression R_squared.ipynb
1.77 KB
How to get Classification AUC ROC.html
271 KB
How to get Classification Accuracy.html
271 KB
How to get Classification Confusion Matrix.html
272 KB
How to get Classification LogLoss Metric.html
271 KB
How to get Classification Report.html
272 KB
How to get Regression MAE.html
272 KB
How to get Regression R_squared.html
272 KB
How to get Regression MSE.html
272 KB
Module 15: Classification Algorithms for Machine Learning
pima-indians-diabetes.data.csv
22.7 KB
CART Algorithm.ipynb
1.65 KB
KNN Algorithm.ipynb
1.65 KB
LDA Algorithm.ipynb
1.8 KB
LR Algorithm.ipynb
1.68 KB
Naive Bayes Algorithm.ipynb
1.64 KB
SVM Algorithm.ipynb
1.59 KB
LDA Algorithm.html
271 KB
LR Algorithm.html
271 KB
Naive Bayes Algorithm.html
271 KB
SVM Algorithm.html
271 KB
CART Algorithm.html
271 KB
KNN Algorithm.html
271 KB
Module 16: Regression Algorithms for Machine Learning
housing.csv
47.9 KB
ElasticNet Algorithm.ipynb
1.73 KB
CART Algorithm.ipynb
1.74 KB
Lasso Algorithm.ipynb
1.7 KB
Linear Regression Algorithm.ipynb
1.72 KB
KNN Algorithm.ipynb
1.73 KB
Ridge Algorithm.ipynb
1.69 KB
SVM Algorithm.ipynb
1.66 KB
KNN Algorithm.html
271 KB
ElasticNet Algorithm.html
271 KB
CART Algorithm.html
271 KB
Linear Regression Algorithm.html
271 KB
Lasso Algorithm.html
271 KB
Ridge Algorithm.html
271 KB
SVM Algorithm.html
271 KB
Module 17: Compare Machine Learning Algorithms
iris.data.csv
4.44 KB
pima-indians-diabetes.data.csv
22.7 KB
Compare Algorithms with diabetes dataset.ipynb
17.5 KB
Compare Algorithms with iris dataset.ipynb
483 KB
Compare Algorithms with diabetes dataset.html
290 KB
Compare Algorithms with iris dataset.html
780 KB
Module 18: Machine Learning Workflows with Pipelines
pima-indians-diabetes.data.csv
22.7 KB
How to create a pipeline that extracts features from the data and create model.ipynb
2.38 KB
How to create a pipeline that standardizes the data and create model.ipynb
2.06 KB
How to create a pipeline that standardizes the data and create model.html
272 KB
How to create a pipeline that extracts features from the data and create model.html
273 KB
Module 19: Machine Learning Ensembles
pima-indians-diabetes.data.csv
22.7 KB
AdaBoost Ensembles.ipynb
14.1 KB
Bagging CART Ensembles.ipynb
12.6 KB
Extra Trees Ensembles.ipynb
11.9 KB
Random Forest Ensembles.ipynb
12.4 KB
Voting Ensembles Ensembles.ipynb
15.3 KB
Gradient Boosting Ensembles.ipynb
14.9 KB
Bagging CART Ensembles.html
284 KB
Gradient Boosting Ensembles.html
286 KB
Extra Trees Ensembles.html
282 KB
Random Forest Ensembles.html
283 KB
AdaBoost Ensembles.html
285 KB
Voting Ensembles Ensembles.html
287 KB
Module 20: Machine Learning with Hyper Parameter Tuning
pima-indians-diabetes.data.csv
22.7 KB
Random Search Cross Validation.ipynb
1.82 KB
Grid Search Cross Validation.ipynb
1.9 KB
Random Search Cross Validation.html
271 KB
Grid Search Cross Validation.html
272 KB
Module 21: Save and Load Machine Learning Models
pima-indians-diabetes.data.csv
22.7 KB
how to save and load model with joblib.ipynb
2.04 KB
how to save and load model with pickle.ipynb
2.14 KB
how to save and load model with joblib.html
272 KB
how to save and load model with pickle.html
272 KB
Module 22: Project on MultiClass Classification
iris.data.csv
4.44 KB
MultiClass Classification.html
378 KB
MultiClass Classification.ipynb
103 KB
Module 23: Project on Binary Classification
sonar.all-data.csv
85.7 KB
Binary Classification.ipynb
1.22 MB
Binary Classification.html
1.51 MB
Module 24: Project on Regression
housing.csv
47.9 KB
Regression.ipynb
278 KB
Regression.html
574 KB

FAQs

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About the Author ( the Data Science Recipe Writer)

I am Nilimesh Halder, the Data Science and Applied Machine Learning Specialist and the guy behind "WACAMLDS: Learn through Codes". I did my PhD in Artificial Intelligence & Decision Analytics from the University of Western Australia (UWA), together with 14+ years of experiences in SQL, R and Python programming & coding. My specialisations are in applied Business Data Science & Forecasting as well as in Transcriptomics Data Science & Bioinformatics.

My objective is to make YOU (Beginners, Developers, Students and Business Professionals) awesome at Data Science, Machine Learning, Deep Learning, Predictive Modelling & Business Analytics. I take the unconventional, application & result oriented top-down approach (i.e. Learn Data Science by Coding without deep diving into the theory) to develop Predictive Models. Here, YOU will find End-to-End "Predictive Modelling & Data Science" Codes / Scripts / Programs suitable for Students, Beginners and Business Professionals. All codes are written in popular programming languages such as Python & R using the widely used Machine Learning frameworks e.g. scikit-learn, XGBoost, CatBoost, LightGBM, TensorFlow, Keras and TuriCreate. Feel Free to connect me at Linkedin.