Curated Learning Tracks & Playlists
Tech Lectures & Courses
Step-by-step course tracks organized strictly by domain and YouTube channel playlist. No mixed lectures, no confusing jumps.
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100 Days of Machine Learning
ML (Machine Learning)CampusXShowing all 134 sequential lectures in this course • Complete curriculum track
20:00
ML (Machine Learning)
What is Machine Learning? | 100 Days of Machine Learning
CampusXWatch
16:02
ML (Machine Learning)
AI Vs ML Vs DL for Beginners in Hindi
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27:42
ML (Machine Learning)
Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth
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11:28
ML (Machine Learning)
Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types
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19:28
ML (Machine Learning)
Online Machine Learning | Online Learning | Online Vs Offline Machine Learning
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16:44
ML (Machine Learning)
Instance-Based Vs Model-Based Learning | Types of Machine Learning
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23:40
ML (Machine Learning)
Challenges in Machine Learning | Problems in Machine Learning
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29:02
ML (Machine Learning)
Application of Machine Learning | Real Life Machine Learning Applications
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25:13
ML (Machine Learning)
Machine Learning Development Life Cycle | MLDLC in Data Science
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26:23
ML (Machine Learning)
Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles
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41:29
ML (Machine Learning)
What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning
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10:00
ML (Machine Learning)
Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML
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10:00
ML (Machine Learning)
End to End Toy Project | Day 13 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively
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10:00
ML (Machine Learning)
Working with CSV files | Day 15 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Working with JSON/SQL | Day 16 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Fetching Data From an API | Day 17 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Understanding Your Data | Day 19 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Pandas Profiling | Day 22 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
What is Feature Engineering | Day 23 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling
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10:00
ML (Machine Learning)
Encoding Categorical Data | Ordinal Encoding | Label Encoding
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10:00
ML (Machine Learning)
One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn
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10:00
ML (Machine Learning)
Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform
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10:00
ML (Machine Learning)
Power Transformer | Box - Cox Transform | Yeo - Johnson Transform
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10:00
ML (Machine Learning)
Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
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10:00
ML (Machine Learning)
Handling Mixed Variables | Feature Engineering
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10:00
ML (Machine Learning)
Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning
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10:00
ML (Machine Learning)
Handling Missing Data | Part 1 | Complete Case Analysis
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10:00
ML (Machine Learning)
Handling missing data | Numerical Data | Simple Imputer
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10:00
ML (Machine Learning)
Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp
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10:00
ML (Machine Learning)
Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4
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10:00
ML (Machine Learning)
KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5
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10:00
ML (Machine Learning)
Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer
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10:00
ML (Machine Learning)
What are Outliers | Outliers in Machine Learning
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10:00
ML (Machine Learning)
Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2
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10:00
ML (Machine Learning)
Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3
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10:00
ML (Machine Learning)
Outlier Detection using the Percentile Method | Winsorization Technique
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10:00
ML (Machine Learning)
Feature Construction | Feature Splitting
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10:00
ML (Machine Learning)
Curse of Dimensionality
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10:00
ML (Machine Learning)
Principle Component Analysis (PCA) | Part 1 | Geometric Intuition
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10:00
ML (Machine Learning)
Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution
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10:00
ML (Machine Learning)
Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization
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10:00
ML (Machine Learning)
Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi
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10:00
ML (Machine Learning)
Simple Linear Regression | Mathematical Formulation | Coding from Scratch
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10:00
ML (Machine Learning)
Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
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10:00
ML (Machine Learning)
Multiple Linear Regression | Geometric Intuition & Code
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10:00
ML (Machine Learning)
Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch
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10:00
ML (Machine Learning)
Multiple Linear Regression | Part 3 | Code From Scratch
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10:00
ML (Machine Learning)
What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression
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10:00
ML (Machine Learning)
Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation
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10:00
ML (Machine Learning)
Batch Gradient Descent with Code Demo | Simple Explanation in Hindi
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10:00
ML (Machine Learning)
Stochastic Gradient Descent
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10:00
ML (Machine Learning)
Mini-Batch Gradient Descent
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10:00
ML (Machine Learning)
Polynomial Regression | Machine Learning
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10:00
ML (Machine Learning)
Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning
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10:00
ML (Machine Learning)
Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models
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10:00
ML (Machine Learning)
Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models
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10:00
ML (Machine Learning)
Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models
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10:00
ML (Machine Learning)
5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models
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10:00
ML (Machine Learning)
Lasso Regression | Intuition and Code Sample | Regularized Linear Models
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10:00
ML (Machine Learning)
Why Lasso Regression creates sparsity?
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10:00
ML (Machine Learning)
ElasticNet Regression | Intuition and Code Example | Regularized Linear Models
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10:00
ML (Machine Learning)
Logistic Regression Part 1 | Perceptron Trick
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10:00
ML (Machine Learning)
Logistic Regression Part 2 | Perceptron Trick Code
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ML (Machine Learning)
Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML
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10:00
ML (Machine Learning)
Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy
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10:00
ML (Machine Learning)
Derivative of Sigmoid Function
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10:00
ML (Machine Learning)
Logistic Regression Part 5 | Gradient Descent & Code From Scratch
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10:00
ML (Machine Learning)
Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1
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10:00
ML (Machine Learning)
Precision, Recall and F1 Score | Classification Metrics Part 2
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10:00
ML (Machine Learning)
ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX
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10:00
ML (Machine Learning)
Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6
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10:00
ML (Machine Learning)
Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7
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10:00
ML (Machine Learning)
Logistic Regression Hyperparameters || Logistic Regression Part 8
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 1 | Conditional Probability
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 2 | Independent Events in Probability
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 3 | Mutually Exclusive Events
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 6 | Intuition
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm
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10:00
ML (Machine Learning)
Naive Bayes Classifier | Part 8 | Simple Example Code
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10:00
ML (Machine Learning)
Naive Bayes Part 9 | Handling Numerical Data
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10:00
ML (Machine Learning)
What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX
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10:00
ML (Machine Learning)
Support Vector Machines | Geometric Intuition
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10:00
ML (Machine Learning)
Mathematics of SVM | Support Vector Machines | Hard margin SVM
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10:00
ML (Machine Learning)
Mathematics of Support Vector Machine | Soft Margin SVM
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10:00
ML (Machine Learning)
Kernel Trick in SVM | Geometric Intuition
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10:00
ML (Machine Learning)
Kernel Trick in SVM | Code Example
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10:00
ML (Machine Learning)
Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain
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10:00
ML (Machine Learning)
Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees
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10:00
ML (Machine Learning)
Regression Trees | Decision Trees Part 3
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10:00
ML (Machine Learning)
Awesome Decision Tree Visualization using dtreeviz library
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10:00
ML (Machine Learning)
Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning
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10:00
ML (Machine Learning)
Voting Ensemble | Introduction and Core Idea | Part 1
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10:00
ML (Machine Learning)
Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2
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ML (Machine Learning)
Voting Ensemble | Regression | Part 3
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ML (Machine Learning)
Bagging | Introduction | Part 1
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10:00
ML (Machine Learning)
Bagging Ensemble | Part 2 | Bagging Classifiers
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10:00
ML (Machine Learning)
Bagging Ensemble | Part 3 | Bagging Regressor
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10:00
ML (Machine Learning)
Introduction to Random Forest | Intuition behind the Algorithm
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10:00
ML (Machine Learning)
How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest
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10:00
ML (Machine Learning)
Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important
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10:00
ML (Machine Learning)
Random Forest Hyper-parameters
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10:00
ML (Machine Learning)
Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example
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10:00
ML (Machine Learning)
OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning
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10:00
ML (Machine Learning)
Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated
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10:00
ML (Machine Learning)
How Adaboost Classifier Works? | Geometric Intuition
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10:00
ML (Machine Learning)
AdaBoost - A Step by Step Explanation
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10:00
ML (Machine Learning)
AdaBoost Algorithm | Code from Scratch
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10:00
ML (Machine Learning)
AdaBoost Hyperparameters | GridSearchCV in Adaboost
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10:00
ML (Machine Learning)
Bagging Vs Boosting | What is the difference between Bagging and Boosting
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10:00
ML (Machine Learning)
Gradient Boosting Explained | How Gradient Boosting Works?
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10:00
ML (Machine Learning)
Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting
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10:00
ML (Machine Learning)
Gradient Boosting for Classification | Geometric Intuition | CampusX
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10:00
ML (Machine Learning)
Introduction to XGBOOST | Machine Learning | CampusX
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10:00
ML (Machine Learning)
XGBoost for Regression | XGBoost Part 2 | CampusX
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10:00
ML (Machine Learning)
XGBoost For Classification | How XGBoost works on Classification Problems | CampusX
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ML (Machine Learning)
The Maths Behind XGBoost | Machine Learning | CampusX
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ML (Machine Learning)
Stacking and Blending Ensembles
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ML (Machine Learning)
K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning
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10:00
ML (Machine Learning)
K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn
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ML (Machine Learning)
K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch
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ML (Machine Learning)
Agglomerative Hierarchical Clustering | Python Code Example
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ML (Machine Learning)
DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX
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ML (Machine Learning)
Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE
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ML (Machine Learning)
Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna
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