Machine Learning Course Curriculum

June 20 - July 15
July 18 - August 12
9:00 am - 10:30 am
S. No. Topic No. of Hours
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


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


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

Day 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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