Machine Learning
4.0
(3)
25 learners
What you'll learn
This course includes
- 8 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 50 lessons • 8 hours of video
Machine Learning
50 lessons
• 7.5 hours
Machine Learning
50 lessons
• 7.5 hours
- Python code for Mean, Median, Mode, SD, Variance and Range 00:48
- Lec-1: Introduction to Data Science & ML | Roadmap to Learn Data Science & ML 08:24
- Lec-2: Supervised Learning Algorithms | Machine Learning 08:50
- Lec-3: Introduction to Regression with Real Life Examples 07:19
- Lec-4: Linear Regression📈 with Real life examples & Calculations | Easiest Explanation 11:01
- Lec-5: Logistic Regression with Simplest & Easiest Example | Machine Learning 10:01
- Lec-6: Linear Regression Vs. Logistic Regression | Supervised Learning | Machine Learning 04:37
- Lec-7: kNN Classification with Real Life Example | Movie Imdb Example | Supervised Learning 10:13
- Lec-8: Naive Bayes Classification Full Explanation with examples | Supervised Learning 13:31
- Lec-9: Introduction to Decision Tree 🌲 with Real life examples 06:07
- Lec-10: Decision Tree 🌲 ID3 Algorithm with Example & Calculations 🧮 16:38
- Lec-11: Conditional Probability with Easiest Explanation & Example 06:23
- Lec-12: Introduction to Ensemble Learning with Real Life Examples | Machine⚙️ Learning 05:58
- Lec-13: K-mean Clustering with Numerical Example | Unsupervised Learning | Machine🖥️ Learning 🙇♂️🙇 07:51
- Lec-14: Hierarchical Clustering | Agglomerative vs Divisive with examples 06:06
- Lec-15: Single Linkage Clustering | Agglomerative Clustering | Hierarchical Clustering 06:16
- Lec-16: Complete Linkage⛓️ Clustering with Example | Clustering in Unsupervised Learning | ML 09:05
- Lec-17: K-medoids Clustering with Numerical Example | Machine Learning 11:53
- Lec-18: Random Forest 🌳 in Machine Learning 🧑💻👩💻 08:33
- Lec-19: kNN for Classified & Regression with Easiest Explanation | Machine Learning 🤖🙇 07:21
- Lec-20: Mean, Median, Mode with Real Life examples | Machine Learning 07:41
- Lec-21: Standard Deviation & Variance with Examples 08:16
- Lec-22: Bagging/Bootstrap Aggregating in Machine Learning with examples 04:56
- Lec-23: Supervised vs Unsupervised learning with real life example 07:31
- Lec-24: How Weights are Increased in Boosting | Ensemble Learning 06:48
- Lec-25: BAGGING vs. BOOSTING vs STACKING in Ensemble Learning | Machine Learning 06:22
- Lec-26: Cross Validation in Machine Learning with Examples 06:51
- Lec-27: Pearson's Correlation Coefficient | Supervised Learning | Data Science & Machine Learning 07:38
- Lec-28: kNN(k Nearest Neighbour) Numerical Example | Supervised Learning | Machine Learning 09:09
- Lec-29: Decision Tree 🌳 Example | Calculate Entropy, Information ℹ️ Gain | Supervised Learning 06:57
- Lec-30: Single Linkage Clustering Example | Unsupervised Learning | Machine Learning 06:52
- Lec-33: How to Deal with Missing Values in DataSet | Data Preprocessing & Data Cleaning 09:27
- Lec-34: kNN Imputation with Examples | Data Preprocessing and Data Cleaning 🧹 07:51
- Lec-35: Fit() & Transform() Method | Data Preprocessing | Machine Learning 06:36
- Lec-36: Feature Extraction in Data preprocessing | Machine Learning 09:21
- Lec-37: Ridge and Lasso Regression | Machine Learning 14:10
- Lec-38: Mean Squared Error (MSE) | Machine learning 09:53
- Lec-39: Multiple Linear Regression (MLR) | Machine Learning 12:48
- Lec-40: Support Vector Machines (SVMs) | Machine Learning 10:23
- Lec-41: Numerical Explanation on SVM | How Support Vector Machine Algorithm Works 16:07
- Lec-42: Linear Discriminant Analysis (LDA) | Machine Learning 13:21
- Lec-43: Bias & Variance Tradeoff Explained: How to Fix Overfitting & Underfitting? 14:44
- Lec-44: K-Fold Cross Validation in Machine Learning 09:52
- Lec-45: Leave-One-Out Cross Validation (LOOCV) Explained with Example | Machine Learning 09:36
- Lec-46: Principal Component Analysis (PCA) Explained | Machine Learning 14:06
- Lec-47: How to update cost in K-Medoid Clustering | Machine Learning 12:05
- Lec-48: Perceptron Learning in ANN | Single Layer Perceptron Model 15:04
- Lec-49: What is Multilayer Perceptron (MLP)? | How It Works in Machine Learning 12:56
- Lec-50: Single Layer Neural Network | Machine Learning 12:03
- New to ML? Follow These Steps to Build Any Machine Learning Model 09:59
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