Basic Machine Learning – Self Paced

The Machine Learning Basics Course 2025 is designed for beginners and aspiring data scientists who want to learn the fundamentals of machine learning, data manipulation, and neural networks using Python. This comprehensive course equips learners with the skills needed to build predictive models, analyze data, and develop AI applications.

Updated as per AICTE norms, the course covers Python programming essentials, data analysis with NumPy and Pandas, visualization with Matplotlib and Seaborn, and machine learning workflows using Scikit-Learn, TensorFlow, and Keras. Students will gain hands-on experience with regression, classification, clustering, model evaluation, and deep learning architectures such as CNNs and RNNs.

Course Curriculum Highlights:

Module 1: Introduction to Python

• Variables, data types, and operators

• Control flow statements and functions

• Lists, tuples, and dictionaries

Module 2: NumPy and Pandas

• Arrays, matrices, and indexing

• Data frames, series, and data manipulation

• Data cleaning and preprocessing

Module 3: Data Visualization

• Line plots, scatter plots, histograms, and box plots

• Heatmaps for correlation and data insights

Module 4: Scikit-Learn

• Preprocessing data and train-test split

• Regression, classification, and clustering algorithms

• Model selection, evaluation, and performance metrics

Module 5: TensorFlow and Keras

• Neural network basics and architecture

• Building CNNs and RNNs

• Transfer learning and real-world applications

By the end of this course, learners will be able to analyze datasets, create machine learning models, visualize insights, and develop deep learning solutions for real-world problems.