Welcome to the ultimate machine learning course in Tamil! In this comprehensive series, dive deep into essential concepts, mathematical foundations, and practical implementation through over 10 hands-on projects. Whether you’re a beginner or looking to refine your skills, this course offers a structured approach to understanding machine learning from scratch.
Introduction to Machine Learning:
Get started with an overview of machine learning and its applications.
Linear Regression & Multivariate Regression:
Master linear regression basics and its multivariate applications.
Linear Regression Performance Metrics:
Learn how to evaluate model performance effectively.
Gaussian Distribution in Machine Learning:
Understand the significance of Gaussian distribution in ML.
Data Preprocessing in ML:
Discover techniques for preparing data for model training.
Gradient Descent Algorithm:
Explore the mechanics of gradient descent optimization.
Classification Problems & Logistic Regression:
Learn about classification problems and logistic regression.
Confusion Matrix & ROC AUC Curve:
Interpret confusion matrices and ROC AUC curves accurately.
Bias-Variance Trade-off & Regularization:
Grasp the concept of bias-variance trade-off and regularization.
Regularization Project with Redwine Quality Data:
Apply regularization techniques to real-world datasets.
Cross Validation Techniques:
Master various cross-validation techniques for model validation.
K Nearest Neighbor (KNN) Algorithm:
Understand KNN algorithm and its applications.
Hyperparameter Tuning:
Learn techniques for optimizing model hyperparameters.
Naive Bayes Classifier:
Unravel the Naive Bayes classifier through a practical project.
Support Vector Machines (SVM):
Master SVM and its applications in classification.
Decision Tree Algorithm:
Gain insights into decision tree algorithms through implementation.
Multiclass Classification & Random Forest Algorithm:
Explore multiclass classification and Random Forest.
Unsupervised Learning & Clustering:
Embark on unsupervised learning with clustering techniques.
Principal Component Analysis (PCA):
Wrap up with a deep dive into PCA for dimensionality reduction.
Join us in this immersive machine learning course designed exclusively for Tamil learners. With detailed explanations in Tamil and comprehensive English descriptions, start your journey today and unlock the endless possibilities of machine learning! Don’t forget to like, share, and subscribe for more content. Happy learning!
Timestamp :
00:00:00 – Introduction to machine learning full course
00:02:07 – Linear regression, multivariate regression & project
00:21:47 – Linear regression performance metrics
00:51:54 – Gaussian or Normal distribution in machine learning
01:13:17 – Data preprocessing(cleaning, feature sccaling etc) in ML
01:30:53 – Gradient Descent algorithm in ML
01:58:45 – Classification problem in ML, Logistic regression & project
02:29:00 – Confusion matrix in machine learning
02:54:03 – ROC AUC Curve in machine learning
03:14:18 – Bias variance, Underfitting Overvitting trade-off in ML
03:37:25 – Regularization in ML (Lasso, Ridge, ElasticNet regression)
04:11:38 – Regularization project using redwine quality data
04:25:13 – Cross Validation Techniques(All types) in ML
04:40:10 – Cross Validation Techniques Project
04:55:46 – K Nearest Neighbor Classifier & Regressor with 2 Projects
05:17:25 – Hyperparameters and Hyperparameter tuning with project
05:46:26 – Naive Bayes Classifier & Project
06:14:16 – Support Vector Machines(soft margin, hard margin, kernel trick)
06:42:50 – Fetal Health Classifcation Using SVM
06:48:43 – Decision Tree Algorithm(Math Explained)
07:18:17 – Decision Tree Implementation & Project
07:24:23 – Multiclass Classification & Project
07:32:41 – Random Forest Algorithm(Bagging, Pasting, Stacking, Adaboost etc)
07:54:11 – Diabetes Prediction Project using Various Classifcation Algorithms
08:09:15 – Intro to Unsupervised Learning, K-means Clustering & Project
08:36:16 – Hierarchical Clustering(Agglomerative & Divisive) & Project
09:00:39 – DBSCAN Clustering & Project
09:17:13 – Clustering Evaluation Metrics & Project
09:33:06 – Principal Component Analysis & 2 Projects
Code : https://github.com/AdityaTheDev/AdiExplains
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