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.
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.
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
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.
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.
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.
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 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.