Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Great Learning

Master Data Science & Machine Learning in Python

via Great Learning

Go to class Write review

Overview

This Python for Data Science and Machine Learning course lays the foundation in Python, with essential libraries such as NumPy and Pandas for data manipulation. You’ll learn to visualize data using Seaborn and Matplotlib and gain hands-on experience with machine learning algorithms, including Linear and Logistic Regression, Decision Trees, Random Forests, and K-Means Clustering. The course covers key techniques like feature engineering, model evaluation, and performance metrics to help you build and assess predictive models effectively.By the end of the course, you'll be able to apply machine learning models to real-world challenges such as customer segmentation, income prediction, and loan approval. Through hands-on projects, you'll gain practical experience in data preprocessing, model building, and evaluation. These projects will prepare you to generate actionable insights and make data-driven decisions, making you ready to solve complex business problems and excel in data science or analytics roles.

Syllabus

  • Key Python Libraries - Numpy
    • Numpy operations such as array indexing and slicing and advanced functions like arithmetic, concatenation, and splitting
  • Key Python Libraries - Pandas
    • Introduction to Pandas, data structures and data manipulation
  • Python Visualization using Seaborn & Matplotlib
    • Introduction to Visualization Libraries
  • EDA for Data Science
    • Data preprocessing techniques, and hands-on case studies to analyze, clean, and transform data effectively.
  • Introduction to Machine Learning
    • Scikit-learn and it's application, with a practical demo in Python.
  • Supervised Learning - Linear Regression
    • Supervised Learning, Linear Regression and Exploratory Data Analysis
  • Supervised Learning - Logistic Regression
    • Logistics Regression concepts including Sigmoid Functions, Confusion Matric, Precision, Recall etx
  • Supervised Learning - Naive Bayes Classifier
    • Bayes theorem, Naive Bayes classifier and hands-on
  • Supervised Learning - Decision Trees
    • Decision Tree - CART algorithm, Entropy, Gini Index.
  • Ensemble Techniques
    • Bagging, Boosting, Random Forest with hands on.
  • Unsupervised Learning
    • Clustering, K-means, Elbow Method, PCA, with hands-on
  • Featurization
    • Feature Engineering, K-Fold, Cross validation, Up and down sampling with hands on.
  • Model Performance Measures
    • Model tuning, Hyper Parameter Tuning, Grid Search, RandomSearch with hands on.
  • Guided Project 1: Income Prediction using Random Forest
    • Income Prediction using Random Forest : Functional requirements and step-by-step guide
  • Guided Project 2: Customer Clustering
    • Customer Clustering : Functional requirements and step-by-step guide
  • Guided Project 3 : Revenue Prediction
    • Revenue Prediction : Functional requirements and step-by-step guide
  • Guided Project 4: Loan Approval using Logistic Regression
    • Loan Approval using Logistic Regression : Functional requirements and step-by-step guide
  • Guided Project 5: Loan Approval Model using Decision Trees
    • Loan Approval Model using Decision Trees : Functional requirements and step-by-step guide
  • Guided Project 6: Movielens Exploratory Data Analysis
    • Movielens Exploratory Data Analysis : Functional requirements and step-by-step guide
  • Machine Learning Engineer - Mock Interview
    • Personalised Mock Interviews to help you land a Machine Learning Engineer role

Taught by

Prof. Mukesh Rao, Dr. Abhinanda Sarkar, and Mr. Bharani Akella

Reviews

Start your review of Master Data Science & Machine Learning in Python

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.