100 Days of Machine Learning | CampusX
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What you'll learn
This course includes
- 62.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 103 lessons • 62.5 hours of video
100 Days of Machine Learning | CampusX
103 lessons
• 41.3 hours
100 Days of Machine Learning | CampusX
103 lessons
• 41.3 hours
- What is Machine Learning? | 100 Days of Machine Learning 20:00
- AI Vs ML Vs DL for Beginners in Hindi 16:02
- Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth 27:42
- Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types 11:28
- Online Machine Learning | Online Learning | Online Vs Offline Machine Learning 19:28
- Instance-Based Vs Model-Based Learning | Types of Machine Learning 16:44
- Challenges in Machine Learning | Problems in Machine Learning 23:40
- Application of Machine Learning | Real Life Machine Learning Applications 29:02
- Machine Learning Development Life Cycle | MLDLC in Data Science 25:13
- Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles 26:23
- What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning 41:29
- Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML 37:06
- End to End Toy Project | Day 13 | 100 Days of Machine Learning 30:43
- How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively 22:22
- Working with CSV files | Day 15 | 100 Days of Machine Learning 36:30
- Working with JSON/SQL | Day 16 | 100 Days of Machine Learning 17:00
- Fetching Data From an API | Day 17 | 100 Days of Machine Learning 22:50
- Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning 37:49
- Understanding Your Data | Day 19 | 100 Days of Machine Learning 15:23
- EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning 30:31
- EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning 38:03
- Pandas Profiling | Day 22 | 100 Days of Machine Learning 13:04
- What is Feature Engineering | Day 23 | 100 Days of Machine Learning 24:52
- Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning 32:38
- Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling 23:31
- Encoding Categorical Data | Ordinal Encoding | Label Encoding 19:53
- One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning 30:12
- Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn 15:41
- Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning 45:39
- Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform 32:13
- Power Transformer | Box - Cox Transform | Yeo - Johnson Transform 21:28
- Binning and Binarization | Discretization | Quantile Binning | KMeans Binning 38:25
- Handling Mixed Variables | Feature Engineering 12:10
- Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning 14:18
- Handling Missing Data | Part 1 | Complete Case Analysis 24:54
- Handling missing data | Numerical Data | Simple Imputer 31:21
- Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp 13:34
- Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4 37:05
- KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5 24:27
- Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer 18:31
- What are Outliers | Outliers in Machine Learning 17:07
- Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2 17:46
- Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3 14:05
- Outlier Detection using the Percentile Method | Winsorization Technique 16:23
- Feature Construction | Feature Splitting 12:22
- Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi 33:36
- Simple Linear Regression | Mathematical Formulation | Coding from Scratch 53:31
- Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score 43:56
- Multiple Linear Regression | Geometric Intuition & Code 20:57
- Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch 48:11
- Multiple Linear Regression | Part 3 | Code From Scratch 16:01
- What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression 17:38
- Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning 08:05
- Logistic Regression Part 1 | Perceptron Trick 47:06
- Logistic Regression Part 2 | Perceptron Trick Code 17:07
- Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML 40:44
- Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy 29:03
- Derivative of Sigmoid Function 05:57
- Logistic Regression Part 5 | Gradient Descent & Code From Scratch 36:42
- Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6 38:21
- Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7 09:11
- Naive Bayes Classifier | Part 1 | Conditional Probability 09:26
- Naive Bayes Classifier | Part 2 | Independent Events in Probability 07:59
- Naive Bayes Classifier | Part 3 | Mutually Exclusive Events 01:49
- Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability 04:27
- Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem 09:00
- Naive Bayes Classifier | Part 6 | Intuition 14:44
- Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm 19:09
- Naive Bayes Classifier | Part 8 | Simple Example Code 16:03
- Naive Bayes Part 9 | Handling Numerical Data 08:47
- Support Vector Machines | Geometric Intuition 11:46
- Mathematics of SVM | Support Vector Machines | Hard margin SVM 34:54
- Mathematics of Support Vector Machine | Soft Margin SVM 14:38
- Kernel Trick in SVM | Geometric Intuition 06:18
- Kernel Trick in SVM | Code Example 14:04
- Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain 58:29
- Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees 27:23
- Regression Trees | Decision Trees Part 3 35:15
- Voting Ensemble | Introduction and Core Idea | Part 1 16:30
- Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2 23:50
- Voting Ensemble | Regression | Part 3 10:57
- Introduction to Random Forest | Intuition behind the Algorithm 33:55
- How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest 12:52
- Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important 12:02
- Random Forest Hyper-parameters 15:17
- Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example 11:44
- OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning 06:45
- Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated 27:20
- How Adaboost Classifier Works? | Geometric Intuition 17:14
- AdaBoost - A Step by Step Explanation 19:23
- AdaBoost Algorithm | Code from Scratch 16:27
- AdaBoost Hyperparameters | GridSearchCV in Adaboost 11:13
- Bagging Vs Boosting | What is the difference between Bagging and Boosting 06:17
- Gradient Boosting Explained | How Gradient Boosting Works? 32:49
- Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting 56:42
- Stacking and Blending Ensembles 35:20
- K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning 23:58
- K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn 10:13
- K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch 33:53
- Agglomerative Hierarchical Clustering | Python Code Example 37:23
- DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX 34:16
- Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE 57:17
- Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna 59:23
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