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This module introduces the fundamentals of Machine Learning, including its meaning, functioning, key features, and importance. It covers major classifications of Machine Learning, its historical development, and the present-day role of Machine Learning in solving real-world problems through data-driven prediction and automated decision-making.
This module explores major applications of Machine Learning, including image and speech recognition, traffic prediction, product recommendation, self-driving cars, stock market prediction, and healthcare. It also covers the complete Machine Learning life cycle, including data collection, data preparation, data wrangling, data analysis, model training and testing, and deployment.
This module introduces different types of data used in Machine Learning, including numerical, categorical, time-series, textual, and image data. It covers the fundamentals and importance of data preprocessing, importing datasets, and feature scaling techniques to prepare data for effective Machine Learning model development.
This module introduces classification algorithms, different types of learners, and major classification approaches used in Machine Learning. It covers model evaluation, the SoftMax function, key performance metrics, and practical use cases for applying classification algorithms to real-world prediction and decision-making problems.
This module introduces regression and linear regression techniques used in Machine Learning. It covers types of linear regression, working principles, advantages, limitations, and practical applications, while also explaining the position of regression within the broader classification of Machine Learning approaches.
This module introduces logistic regression and its importance in classification problems. It covers differences between linear and logistic regression, the sigmoid function, mathematical modeling, visualization, applications, advantages, limitations, evaluation metrics, and practical examples for predicting categorical outcomes
This module introduces the Support Vector Machine (SVM) algorithm, its key terms, objectives, and hyperplanes as decision surfaces. It covers linear and non-linear classification, types of SVM, advantages, disadvantages, and real-world applications for effective classification and predictive modeling.
This module introduces kernel tricks in Support Vector Machines and explains their need for handling non-linear data. It covers the working mechanism of kernel functions, key parameters such as gamma, major types of kernels, and practical implementation for solving complex classification problems.
This module introduces the Decision Tree classification algorithm, its meaning, purpose, and working process. It covers attribute selection measures such as information gain, entropy, and Gini index, along with advantages, disadvantages, and Python implementation for solving classification problems.
This module introduces ensemble learning, its working principles, and challenges in developing ensemble models. It covers the Random Forest classifier, its assumptions, features, need, and functioning, along with practical applications, advantages, disadvantages, and implementation for effective classification and predictive modeling.
This module introduces the K-Nearest Neighbour (KNN) algorithm, its step-by-step procedure, and working mechanism. It covers practical examples, selection of the optimal K value, advantages, disadvantages, real-world applications, and Python implementation for classification and prediction tasks.
This module introduces elementary probability, Bayes’ Theorem, and Bayesian classification with practical examples. It covers the prerequisites, key terms, and types of Naïve Bayes classifiers, along with their advantages, disadvantages, real-world applications, and implementation for probabilistic classification tasks.
This module introduces K-Means clustering as an unsupervised Machine Learning technique. It covers the working process of the algorithm, selection of the optimal K value, the Elbow Method, advantages, limitations, and real-world applications for data grouping, segmentation, and pattern discovery.
This module introduces Reinforcement Learning algorithms, their basic framework, key components, core pillars, and workflow. It covers the mathematical foundations of Reinforcement Learning, exploration versus exploitation, evolution of RL capabilities, advantages, limitations, industrial applications, and comparison with other Machine Learning paradigms.
This course introduces the fundamentals of Machine Learning, including data types, data preprocessing, regression, classification, clustering, ensemble learning, Support Vector Machines, Decision Trees, K-Nearest Neighbours, Naïve Bayes, Random Forest, and Reinforcement Learning. It equips learners with practical skills to prepare data, build and evaluate Machine Learning models, implement algorithms using Python, and develop effective real-world predictive solutions.
Learning Objectives:
By the end of the course, you will be able to:
Why This Course Matters:
