Course Link: https://www.trycle.com/courses/machine-learning-and-data-science-using-python Course Materials: All the Jupyter Notebooks of classes and Exercises are uploaded above, All csv and excel sheets of data also included in corresponding folders. By Clicking on code button and selecting Download ZIP, You can download the entire course materials to your system.

          THE TOPICS WE COVER

#1-1 How to install Python? #1-2 What is a Module? #1-3 Py Files #1-4 For Loops #1-5 For Loop Samples #1-6 Strings, Int and Input #1-7 If, Else and Elif #1-8 While Loops #1-9 While Infinite loops #1-10 While Loop Sample #1-11 Functions #1-12 Global and Local Variables #1-13 Variables #1-14 Data Types and Operators #1-15 String Operations,Special Operators #1-16 List in Python #1-17 Tuple and Set #1-18 Dictionary #1-19 Class and Objects #1-20 File Handling #1-21 Exception Handling #1-22 Working with JSON #1-23 Session 1: Conclusion

#2-1 Jupyter Notebook #2-2 Numpy Module #2-3 Numpy Arrays #2-4 Pandas Introduction #2-5 Data Frame Creation and Views #2-6 Data Frame Operations #2-7 Creating Data Frames #2-8 Read Write CSV Files #2-9 Read Write EXCEL Files #2-10 Handling missing data- Fillna #2-11 Interpolate and Dropna #2-12 Replace Function #2-13 Group By #2-14 Concat and Merge #2-15 Matplotlib Introduction #2-16 Format Strings in Plot Function #2-17 Labels, Legend and Grid #2-18 Bar Charts #2-19 Histograms #2-20 Pie Chart and Save plot images #2-21 Session 2: Conclusion

#3-1 Linear Regression #3-2 Linear Regression Multivariate #3-3 How Gradient Descent #3-4 Gradient Descent Implementation #3-5 Save and Load model #3-6 Dummy Variables #3-7 One Hot Encoding #3-8 Train Test Split #3-9 Logistic Regression with Logit Function #3-10 Logistic Regression Binary Classification #3-11 Logistic Regression MultiClass #3-12 Confusion Matrix #3-13 How Decision Tree #3-14 Decision Tree Implementation #3-15 Random Forest #3-16 Support Vector Machine #3-17 SVM Classifier(SVC) #3-18 K-Fold Cross Validation #3-19 K-Fold and Parameter Tuning #3-20 K-Means Clustering #3-21 K-Means Implementation #3-22 Session 3: Conclusion

#4-1 Data Cleaning #4-2 Feature Engineering #4-3 Outlier Removal #4-4 Outlier Removal Contnd #4-5 Model Building #4-6 Model Export #4-7 Pycharm and VSC Editors #4-8 Python Flask Server #4-9 Flask Server Codes #4-10 Flask Server Running #4-11 Website UI #4-12 Session 4: Conclusion #5 Course: Conclusion