Data Science & Machine Learning in Python

Get the skills needed to perform data science and machine learning tasks using the Python programming language
  • Intermediate
  • Online Classes
  • Total 12 hours in class
  • Data Science & Machine Learning in Python
  • Multi-Day Course
  • 2-6 learners per class


Basic programming knowledge in Python, familiarity with libraries like Numpy, Pandas, and Matplotlib. Our Python Programming for Beginners and Data Analysis with Python Training Courses cover all of the prerequisites.

Class Details

This course is designed to provide students with the knowledge and skills needed to perform data science and machine learning tasks using Python programming language. Students will learn to apply machine learning and deep learning algorithms using the libraries such as scikit-learn, Kears, and Tensorflow. The course will cover topics such as data cleaning, exploratory data analysis, machine learning algorithms, model evaluation, and selecting the appropriate model for business use.

Learning Objectives:

  • Understand the fundamental concepts of data science and machine learning
  • Perform exploratory data analysis and visualization using Matplotlib and Seaborn libraries
  • Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests
  • Understand the basics of deep learning and its applications in Python

Course Outline

  1. Introduction to Data Science and Machine Learning in Python
    • Overview of data science and machine learning
    • Introduction to Python for data science and machine learning
    • Introduction to Google Colab Notebook
  2. Exploratory Data Analysis and Feature Selection
    • Data visualization techniques
    • Data exploration with Pandas and Matplotlib
    • What are features and how it is important
  3. Machine Learning Basics
    • Introduction of Overfitting and Underfitting
    • Class Imbalance and its solution
    • Download the dataset from Kaggle
    • Data splitting and data preparation for the Mchine learning model
    • Machine learning Basics and Types
    • Supervised, Unsupervised, Reinforcement learning with example
  4. Supervised Learning Algorithms
    • Linear regression
    • Logistic regression
    • Decision trees and random forests
    • Project: Wine Quality Predictions
  5. Unsupervised Learning Algorithms
    • K-means clustering
    • Hierarchical clustering
    • Dimensionality reduction
    • Project: Iris Classification
  6. Model Evaluation and Selection
    • Model evaluation metrics
    • Cross-validation
    • Model selection techniques
    • Ensemble Model
    • Model save and load
  7. Deep Learning with Keras and TensorFlow
    • Introduction to deep learning
    • Neural networks
    • Keras and TensorFlow
  8. Convolutional Neural Networks (CNN)
    • CNN basics
    • CNN implementation using Keras
    • Project: Cats and Dogs Classification
  9. Recurrent Neural Networks (RNN)
    • RNN basics
    • RNN implementation using Keras
    • Project: Sentiment analysis using text data
  10. Real-world Examples
    • Some real-life business applications of Machine learning
    • Q&A and course feedback



Priyanka Sharma

Friendly, Attentive, and Engaging Life Skills Teacher

Priyanka has around 14 years of experience in software development, testing and mentoring freshers & experienced professionals. She is an Oracle Ce...
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Younus Kazi

A professional Online and Classroom Teacher

Younus has over 18 years experiences in working and teaching IT in both public and private organisations. His specialities include Web design, deve...
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Jayan Karmacharya

An experienced programmer and online Educator

Jayan is an Experienced Web Developer and IT tutor with over 10 years of experience in Server side programming, front-end development, database des...
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