Topic 01:
Python (Internals, do’s and don’ts) Architecture, Data Structure
Sub-Topic Installation of Anaconda Prompt Jupyter Notebook-An Overview Shorcut Lkeys in Jupyter Notebook Data Types in Python Rules for Naming the Variables List Tuple Set Dictionary
Topic 02:
Data Analysis , Manipulation with numpy and pandas Python data science package to manipulate, calculate and analyze data
Sub-Topic Machine Learning Libraries Numpy-Hands on Pandas-Hands on
Topic 03:
Exploratory Data Visualization in Python with matplotlib Learn how to explore, visualize, and extract insights from data
Sub-Topic Data Visualization Matplotlib-Hands on Seaborn-Hands on
Topic 04:
Statistical Thinking in Python (Part 1) Build the foundation you need to think statistically and to speak the language of your data
Sub-Topic Measures of Central Tendency Measures of Dispersion IQR Statistics-Hands-On
Topic 05:
Supervised Learning and UnSupervised Learning Classification, Regression, Fine-tuning your model
Sub-Topic Supervised Learning Unsupervised Learning Linear Regression Metrics in Linear Regression Hands-on in Linear Regression
Topic 06:
Logistic regression
Sub-Topic Logistic Regression Metrics in Logistic Regression Hands-on in Logistic Regression
Topic 07:
SVM, Linear Regression
Sub-Topic Support Vector Machine Hands on in SVM
Topic 08:
Preprocessing for Machine Learning in Python Introduction to Data Preprocessing, Standardizing Data
Sub-Topic Exploratory Data Analysis Missing Values Outliers Standardization Mnormalization Feature Scaling and Selection
Topic 09:
Tree Based Models Classification and Regression Trees
Sub-Topic Decision Tree Bagging Boosting Random Forest
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