This course on Machine Learning aims to teach learners the following:
- Understand the basics of machine learning and its real-life applications
- Learn about classic and adaptive machine learning techniques
- Explore feature selection, overfitting, underfitting, and regression analysis
- Gain knowledge of classification approaches, principal component analysis, and ensemble learning
- Master algorithms such as Naive Bayes, Support Vector Machines, Decision Trees, and clustering methods like K-means and DBSCAN
- Learn about recommendation systems, bias-variance trade-off, and data preprocessing techniques
- Develop skills in data visualization, loading data using Pandas, and implementing various machine learning algorithms using Python
- Understand concepts like conditional probability, Bayes Theorem, backpropagation, and ensemble methods
- Acquire the ability to work with different types of regression models and classification algorithms
The course uses a combination of theoretical explanations, solved examples, and hands-on Python programming to help learners grasp the concepts effectively. It is designed for individuals interested in diving deep into machine learning, data analysis, and predictive modeling.
Overview
Syllabus
Introduction To Machine Learning ll Machine Learning Course Explained With RealLife Examples (Hindi).
Classic Machine And Adaptive Machine ll Machine Learning Course Explained in Hindi.
Basics Of Training And Testing Phase ll Machine Learning Course Explained in Hindi.
Overfitting and Underfitting Explained with Examples in Hindi ll Machine Learning Course.
Feature Selection Techniques Explained with Examples in Hindi ll Machine Learning Course.
MultiClass Classification Approaches ll One Vs All and One Vs One Classification Explained in Hindi.
Basics Of Principal Component Analysis Explained in Hindi ll Machine Learning Course.
Principal Component Analysis(PCA) Part-2 Explained with Solved Example in Hindi l Machine Learning.
Principal Component Analysis Part-3 Explained with Solved Explained in Hindi l Machine Learning.
Regression Analysis l Dependent And Independent Variables (HINDI).
Linear Regression And Logistic Regression Explained in HINDI.
Confusion Matrix ll Accuracy,Error Rate,Precision,Recall Explained with Solved Example in Hindi.
Curse Of Dimensionality Explained with Examples in Hindi ll Machine Learning Course.
Managing Missing Features Explained with Examples in Hindi ll Machine Learning Course.
Managing Categorial Data Explained with Examples in Hindi ll Machine Learning Course.
Linear Regression Explained in Hindi ll Machine Learning Course.
Logistic Regression Explained in Hindi.
Polynomial Regression Explained in Hindi ll Machine Learning Course.
Ridge Regression Explained in Hindi ll Machine Learning Course.
Lasso Regression Explained in Hindi ll Machine Learning Course.
Elastic Net Regression Explained in Hindi ll Machine Learning Course.
Conditional Probability Explained with Solved Example and Sample Space in Hindi.
Bayes Theorem Explained with Solved Example in Hindi ll Machine Learning Course.
Naive Bayes Classifier ll Data Mining And Warehousing Explained with Solved Example in Hindi.
Back Propagation Algorithm /Back Propagation Of Error (Part-1)Explained With Solved Example in Hindi.
Back Propagation Algorithm (Part-2) Explained with Solved Example in Hindi.
Back Propagation Algorithm (Part-3) Explained With Solved Example in Hindi.
Back Propagation Algorithm (Part-4) Explained with Solved Example in Hindi.
Naive Bayes Variants : Bernoulli Naive Bayes l Bernoulli Distribution Explained in Hindi.
Naive Bayes Variants : Multinomial Naive Bayes l Multinomial Distribution Explained in Hindi.
Naive Bayes Variants : Gaussian Naive Bayes Explained in Hindi.
Support Vector Machine (SVM) Part-1 ll Machine Learning Course Explained in Hindi.
Support Vector Machine (SVM) Part-2 ll Machine Learning Course Explained in Hindi.
Non-Linear Support Vector Machine (SVM) And Kernel Function ll Machine Learning Course in Hindi.
Decision Tree Algorithm Part-1 Explained With Example ll DMW ll ML Easiest Explanation Ever in Hindi.
Decision Tree Algorithm Part-2 ll Constructing of Decision Tree ll ML ll DMW Explained in Hindi.
Ensemble Learning l Machine Learning Course Easiest Explanation Ever in Hindi.
Ensemble Method : Bagging (Bootstrap Aggregation) l Machine Learning Course in Hindi.
Ensemble Method : Boosting ll Machine Learning Course Explained in Hindi.
Voting Classifier : Hard Voting and Soft Voting Explained with Examples in Hindi ll Machine Learning.
Random Forest Step-Wise Explanation ll Machine Learning Course Explained in Hindi.
DBSCAN (Density Based Spatial Clustering Of Applications with Noise) ll Machine Learning (Hindi).
K means Clustering Algorithm Explained With an Example Easiest And Quickest Way Ever In Hindi.
Hierarchical Clustering : Agglomerative Clustering and Divisive Clustering Explained in Hindi.
Agglomerative Clustering (Single Linkage) Part-1 Explained with Solved Example in Hindi.
Agglomerative Clustering (Single Linkage) Part-2 Explained with Solved Example in Hindi.
Agglomerative Clustering (Complete Linkage) Explained with Solved Example in Hindi.
Recommendation System : Content Based Recommendation and Collaborative Filtering Explained in Hindi.
Data in Machine Learning Explained in Hindi.
How Much Data Do I Need in Machine learning Explained in Hindi.
Bias and Variance Explained in Hindi l Machine Learning Course.
Bias-Variance Trade-Off Explained in Hindi l Machine Learning Course.
Linear Regression Solved Numerical Part-1 Explained in Hindi l Machine Learning Course.
Linear Regression Solved Numerical Part-2 Explained in Hindi l Machine Learning Course.
Recurrent Neural Network (RNN) Part-1 Explained in Hindi.
Recurrent Neural Network (RNN) Part-2 Explained in Hindi.
Loading Data Using Pandas Explained in Hindi l Machine Learning Course.
Understanding Data Using Statistics Explained in Hindi l Machine Learning Course.
Visualization of Data Using Matplotlib Part-1 Explained in Hindi l Machine Learning Course.
Visualization of Data Using Matplotlib Part-2 Explained in Hindi l Machine Learning Course.
Data Preprocessing Techniques : Normalization Explained with Python in Hindi l Machine Learning.
Data Preprocessing Techniques : Standardization Explained with Python in Hindi l Machine Learning.
Data Preprocessing Techniques : Binarization Explained with Python in Hindi l Machine Learning.
Training and Testing data Explained in Hindi with Python in Hindi l Machine Learning Course.
Linear Regression Single Variable Explained with Python in Hindi l Machine Learning Course.
Linear Regression Multiple Variables Explained with Python in Hindi l Machine Learning Course.
Logistic Regression Explained with Python in Hindi l Machine Learning Course.
K Means Clustering Implementation with Python Part-1 Explained in Hindi l Machine Learning Course.
K Means Clustering Implementation with Python Part-2 Explained in Hindi l Machine Learning Course.
Taught by
5 Minutes Engineering
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DARSHAN B URK21EC6014"Introduction to Machine Learning" offers a comprehensive dive into the fundamentals of machine learning techniques, algorithms, and applications. The course covers topics such as supervised and unsupervised learning, regression, classification, clu…
