Machine Learning Course Curriculum

  • 02 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