AI with Python

Categories: After SEE, IT Training
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About Course

This comprehensive 2-month course is designed to take students from the fundamentals of Python programming to developing their own predictive models using machine learning. By the end of the course, students will have built a strong foundation in Python, gained hands-on experience with essential machine learning libraries, and developed the skills to implement and build predictive models from scratch. Through practical projects and real-world applications, students will be equipped to apply their knowledge confidently in various domains.

What Will You Learn?

  • Basics of Python Programming – Understanding simple syntax, loops, functions, and data types.
  • Introduction to AI & Machine Learning – What AI is, how machines learn, and real-world applications.
  • Machine Learning Libraries – Hands-on practice with beginner-friendly libraries like Scikit-learn and Pandas.
  • Data Collection & Cleaning – Understanding datasets, handling missing values, and preparing data.
  • Data Visualization – Using graphs and charts (Matplotlib & Seaborn) to understand data better.
  • Feature Engineering – Simple techniques to improve data quality for better predictions.
  • Machine Learning Algorithms – Basics of decision trees, regression, and classification models.
  • Building a Simple AI Model – Step-by-step process of creating and testing a predictive model.

Course Content

Syllabus+ Demo Videos
This consists of the syllabus and trial videos for web development, which will help you get started on your web development journey.

Week 1
In Week 1, learners will build a foundation in Python, covering essential syntax, operators, variables, and data types. They will also set up their development environment and explore programming fundamentals, ensuring a smooth start to the course.

Week 2
In Week 2, learners will explore loops, functions, lists, and dictionaries. They will also learn about Python modules, enhancing their coding skills and progressing toward structured programming.

Week 3
In Week 3, learners will explore essential machine learning libraries for data handling, manipulation, and visualization. They will gain hands-on experience with key libraries, enhancing their ability to work with data and advancing there ways for building foundational models

Week 4
In Week 4, learners will dive deeper into data visualization and analysis. They will focus on data preprocessing and feature engineering techniques, gaining essential skills to prepare and transform data for effective machine learning model development.

Week 5
In Week 5, learners will explore machine learning algorithms, diving deeper into supervised learning. They will study algorithms from linear regression to decision trees, and understand model evaluation techniques and performance metrics to assess the effectiveness of their models.

Week 6
In Week 6, learners will grasp unsupervised learning, focusing on clustering algorithms and PCA. They will also advance to neural networks and understand backpropagation, building a deeper understanding of how neural networks learn and optimize during training.

Week 7
In Week 7, learners will dive into reinforcement learning and deep learning, building models using TensorFlow. They will practice implementing algorithms on real-world datasets and evaluating model performance. Additionally, they will learn the basics of API building with Flask.

Week 8
Week 8 marks the course's conclusion, where learners will receive a compilation of all datasets, practice problems, and assignments to further enhance their skills. Attractive rewards will be given to top performers, recognizing their achievements throughout the course.

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