Machine Learning Full Course with Projects (Beginners to Advanced Level)

Lesson 1: Machine Learning Kya Hai? | Opportunities, Advantage & Classification of Machine Learning

This video introduces machine learning, covering what it is, the opportunities it offers, and its advantages. It also explains the classification of machine learning into different types.

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Lesson 2: Top 10 Applications of Machine Learning in Day-to-Day Life | Machine Learning Application Examples

This video shows ten real-world applications of machine learning in daily life, helping you see how ML is used in practical scenarios.

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Lesson 3: Machine Learning Life-cycle Explained - Complete Information

This video explains the complete machine learning lifecycle, from data collection to model deployment, giving you a high-level view of the ML process.

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Lesson 4: Install Anaconda and Python on Windows (Latest Version) | Machine Learning

This tutorial walks you through installing Anaconda and Python on Windows, setting up your environment for machine learning development.

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Lesson 5: Artificial Intelligence vs Machine Learning vs Deep Learning | Machine Learning Tutorial

This video clarifies the differences between artificial intelligence, machine learning, and deep learning, and how they relate to each other.

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Lesson 6: What is Dataset & Types of Datasets? | Machine Learning - सम्पूर्ण जानकारी

This video explains what datasets are and the different types of datasets used in machine learning, including training and testing sets.

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Lesson 7: Data Preprocessing Kya Hai? | Techniques & Steps of Data Preprocessing | Machine Learning

This video covers data preprocessing, including techniques and steps to clean and prepare data for machine learning models.

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Lesson 8: What are Features and Labels in Machine Learning? (with Example) | Machine Learning Tutorial

This short video explains features and labels in machine learning with examples, which are fundamental concepts for building models.

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Lesson 9: What is Supervised Machine Learning? Types, Advantages & Disadvantages of Supervised Learning

This video introduces supervised machine learning, its types, and the advantages and disadvantages of using this approach.

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Lesson 10: What is Unsupervised Machine Learning? Association & Clustering Algorithms in Machine Learning

This video explains unsupervised machine learning, focusing on association and clustering algorithms, and how they differ from supervised learning.

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Lesson 11: Training & Testing Data in Machine Learning | Complete Information

This video provides complete information about training and testing data in machine learning, including why splitting data is important.

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Lesson 12: Linear Regression Single Variable | Machine Learning Tutorial

This tutorial covers linear regression with a single variable, explaining the concept and how to implement it.

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Lesson 13: Linear Regression Multiple Variable | Machine Learning Tutorial

This video explains linear regression with multiple variables, extending the single variable case to handle more complex datasets.

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Lesson 14: Machine Learning Polynomial Regression Explained | ML Tutorial for Beginners

This video explains polynomial regression, a technique for modeling non-linear relationships, and how it fits into machine learning.

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Lesson 15: Logistic Regression (Binary Classification) | Machine Learning Tutorial

This video covers logistic regression for binary classification, including the concept and practical implementation.

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Lesson 16: Logistic Regression (Multiclass Classification) | Machine Learning Tutorial

This video extends logistic regression to multiclass classification, showing how to handle problems with more than two classes.

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Lesson 17: Decision Tree Classification in Machine Learning | Decision Tree in ML

This video explains decision tree classification, how the algorithm works, and its use in machine learning.

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Lesson 18: Random Forest Classification in Machine Learning | Random Forest Tutorial

This video covers random forest classification, an ensemble method that combines multiple decision trees for better performance.

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Lesson 19: Naive Bayes Classifier Algorithm in Machine Learning | Machine Learning Tutorial

This video explains the naive Bayes classifier algorithm, a probabilistic method for classification, and its applications.

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Lesson 20: Support Vector Machine Algorithm in Machine Learning | Machine Learning Tutorial

This video covers support vector machines (SVM), a powerful classification algorithm, and how it works.

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Lesson 21: Confusion Matrix In Machine Learning | Machine Learning Tutorial

This video explains the confusion matrix, a tool for evaluating classification model performance, including metrics like accuracy and precision.

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Lesson 22: Overfit & Underfit in Machine Learning | Machine Learning Tutorial for Beginners

This video covers overfitting and underfitting in machine learning, explaining what they are and how to address them.

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