Python for Machine Learning

The "Python for Machine Learning" course is designed to provide participants with a comprehensive introduction to the fundamental concepts of machine learning and the practical skills required to implement them using the Python programming language. This course aims to cater to individuals with varying levels of expertise, from beginners with minimal programming experience to those familiar with Python looking to delve into the world of machine learning.

Starting From: 22nd January 2024

Course Duration: 20 Hours

Class Timings: 6:50PM to 9:20PM

Course Fee: ₹20,000/-


About Course

Course Objectives:

  • Foundations of Machine Learning:

    • Understanding the core principles and types of machine learning (supervised learning, unsupervised learning, and reinforcement learning).
    • Exploring real-world applications of machine learning in various industries.

  • Python Programming Fundamentals:

    • Brushing up on essential Python programming skills for machine learning.
    • Covering data types, control structures, functions, and basic libraries (NumPy, Pandas) commonly used in machine learning.

  • Data Preprocessing and Exploration:

    • Learning the importance of data quality and preprocessing.
    • Exploring techniques for handling missing data, scaling features, and encoding categorical variables.
    • Utilizing data visualization tools (Matplotlib, Seaborn) for exploratory data analysis.

  • Supervised Learning Algorithms:

    • Comprehensive coverage of popular supervised learning algorithms such as linear regression, decision trees, support vector machines, and k-nearest neighbors.
    • Hands-on implementation and evaluation of these algorithms using scikit-learn.

  • Unsupervised Learning Techniques:

    • Introduction to unsupervised learning with clustering algorithms like K-means and hierarchical clustering.
    • Dimensionality reduction techniques including Principal Component Analysis (PCA).

  • Model Evaluation and Hyperparameter Tuning:

    • Understanding the metrics used to evaluate machine learning models.
    • Exploring techniques for fine-tuning model parameters to enhance performance.

  • Introduction to Neural Networks and Deep Learning:

    • Overview of neural networks and their role in deep learning.
    • Building and training simple neural networks using TensorFlow or PyTorch.

  • Practical Project Work:

    • Applying acquired knowledge to real-world projects.
    • Collaborative problem-solving and code reviews.

Course Format:

The "Python for Machine Learning" course employs a combination of lectures, hands-on coding exercises, and projects to reinforce learning. Participants are encouraged to actively engage with the material and seek assistance through Q&A sessions and forums.

By the end of the "Python for Machine Learning" course, participants will have gained a solid foundation in machine learning principles and practical experience in implementing machine learning solutions using Python, empowering them to tackle real-world challenges in this rapidly evolving field.


Earn a Certificate upon completion

Online Classes

Start instantly and learn at your own

Life Time Accessibility

Set and maintain flexible deadlines.

Beginner Level

No prior experience required.

Program Curriculum
  • Introduction to ML
  • Supervised Learning Overview
  • Classification Error Metrics
  • Regression Error Metrics
Machine Learning Foundation
  • Linear Regression Theory
  • Linear Regression in Python
  • Linear Regression Project
  • Cross validation and Bias Variance Trade Off
  • Logistic Regression Theory
  • Logistic Regression in Python
  • Logistic Regression Project
  • KNN Theory
  • KNN with Python
  • KNN Project

Our Program Focuses On Three Key Areas

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