Machine Learning Course With Python
4.0
(3)
40 learners
What you'll learn
This course includes
- 79.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 141 lessons • 79.3 hours of video
Machine Learning Course With Python
141 lessons
• 77.5 hours
Machine Learning Course With Python
141 lessons
• 77.5 hours
- Machine Learning Course curriculum | Machine Learning - Roadmap09:24
- 1.1 AI vs Machine Learning vs Deep Learning | AI vs ML vs DL | Machine Learning Training with Python05:35
- 1.2. Supervised vs Unsupervised vs Reinforcement Learning | Types of Machine Learning07:04
- 1.3. Supervised Learning | Types of Supervised Learning | Machine Learning Tutorial06:13
- 1.4. Unsupervised Learning | Clustering and Association Algorithms in Machine Learning08:03
- 1.5. What is Deep Learning | Deep Learning Tutorial | Deep Learning Simplified08:40
- 2.1. Google Colaboratory for Python | Getting started with Google Colaboratory | Google Colab basics10:17
- 2.2. Python Basics | Python Tutorial For Beginners | Learn Python Programming from Scratch23:04
- 2.3. Python Basic Data Types | Python Tutorial | int float string complex boolean20:40
- 2.4. Python Special data types | List Tuple Set Dictionary | Python Tutorial27:04
- 2.5. Operators in Python | Python Tutorial |Arithmetic Assignment Comparison Logical Identity Member19:29
- 2.6. if else statement in Python | if else | if elif else | nested if statement | Python Tutorial13:59
- 2.7. Loops in Python | For Loop in Python | While Loop in Python | Python Tutorial15:50
- 2.8. Functions in Python | Python Tutorial for Beginners15:12
- 3.1. Complete Numpy Tutorial in Python | Numpy Arrays45:52
- 3.2. Complete Pandas Tutorial in Python | Pandas Dataframe Tutorial47:04
- 3.3. Matplotlib Tutorial in Python | Machine Learning Course with Python30:54
- 3.4. Seaborn Tutorial in Python | Machine Learning Course35:56
- 4.1. Where to Collect Data For Machine Learning? | Data Collection13:26
- 4.2. Importing Datasets through Kaggle API14:29
- 4.3. Handling Missing Values in Machine Learning | Imputation | Dropping21:59
- 4.4. Data Standardization | Data Preprocessing | Machine Learning Course20:14
- 4.5. Label Encoding | Data Pre-Processing | Machine Learning Course19:18
- 4.6. Train Test Split | Splitting the dataset to Training and Testing data | Machine Learning Course12:32
- 4.7. How to Handle imbalanced Dataset | Data Pre-Processing | Machine Learning Course19:10
- 4.8. Feature extraction of Text data using Tfidf Vectorizer | Data Preprocessing | Machine Learning11:58
- 4.9. Numerical Dataset Pre-Processing - Use Case | Machine Learning Course with Python20:35
- 4.10. Text Dataset Pre-Processing - Use Case | Machine Learning Course | Data Pre Processing36:21
- Project 1 : SONAR Rock vs Mine Prediction with Python | End To End Python Machine Learning Project49:33
- Project 2: Diabetes Prediction using Machine Learning with Python | End To End Python ML Project58:11
- Project 3. House Price Prediction using Machine Learning with Python | Machine Learning Project56:28
- Project 4. Fake News Prediction using Machine Learning with Python | Machine Learning Projects01:11:50
- Project 5. Loan Status Prediction using Machine Learning with Python | Machine Learning Project01:08:55
- Project 6. Wine Quality Prediction using Machine Learning with Python | Machine Learning Project58:09
- Project 7. Car Price Prediction using Machine Learning with Python | Machine Learning Projects47:53
- Project 8. Gold Price Prediction using Machine Learning with Python | Machine Learning Projects40:55
- Project 9. Heart Disease Prediction using Machine Learning with Python | Machine Learning Projects42:55
- Project 10. Credit Card Fraud Detection using Machine Learning in Python | Machine Learning Projects49:35
- Project 11. Medical Insurance Cost Prediction using Machine Learning with Python | ML Projects01:05:09
- Project 12. Big Mart Sales Prediction using Machine Learning with Python | Machine Learning Projects01:17:11
- Project 13. Customer Segmentation using K-Means Clustering with Python | Machine Learning Projects49:47
- Project 14. Parkinson's Disease Detection using Machine Learning - Python | Machine Learning Project01:09:58
- Project 15. Titanic Survival Prediction using Machine Learning in Python | Machine Learning Project01:13:12
- Project 16. Calories Burnt Prediction using Machine Learning with Python | Machine Learning Projects01:12:32
- 5.0. Mathematics for Machine Learning - Introduction | Machine Learning Course06:05
- 5.1.1. Linear Algebra - Vectors | Mathematics for Machine Learning10:19
- 5.1.2. Vector Operations - Part 1 | Mathematics for Machine Learning | Linear Algebra13:43
- 5.1.3. Vector Operations - in Python - Part 1 | Math for Machine Learning | Linear Algebra19:53
- 5.1.4. Vector Operations - Part 2 | Dot Product | Cross Product | Projection of vector | Math for ML10:24
- 5.1.5. Vector Operations - in Python - Part 2 | Dot Product | Cross Product | Projection of vector18:59
- 5.1.6. Matrix - Basics | Math for Machine Learning | Linear Algebra14:58
- 5.1.7. Working with Matrix in Python | Mathematics for Machine Learning | Linear Algebra19:28
- 5.1.8. Matrix Operations - Addition, Subtraction, Multiplication | Mathematics for Machine Learning19:56
- 5.1.9. Matrix Operations in Python | Mathematics for Machine Learning | Linear Algebra32:25
- 5.2.1. Statistics for Machine Learning | Machine Learning course08:58
- 5.2.2. Basics of Statistics | Types of Data in Statistics | Statistics for Machine Learning13:40
- 5.2.3. Types of Statistics | Descriptive and Inferential Statistics | Machine Learning Course14:05
- 5.2.4. Types of statistical studies | Statistics for Machine Learning | Machine Learning course12:24
- 5.2.5. Population and Sample | Sampling techniques | Statistics for Machine Learning23:38
- 5.2.6. Measure of Central Tendencies - Mean, Median, Mode | Statistics for Machine Learning16:32
- 5.2.7. Measure of Variability - Range, Variance, Standard Deviation | Math for Machine Learning12:54
- 5.2.8. Percentiles and Quantiles | Statistics for Machine Learning | Machine Learning Course08:56
- 5.2.9. Correlation and Causation | Statistics for machine learning | Machine Learning Course13:43
- 5.2.10. Hypothesis Testing | Null Hypothesis and Alternative Hypothesis | Math For Machine Learning10:12
- 5.3.1. Probability for Machine Learning | Machine Learning Course08:27
- 5.3.2. Basics of Probability | Probability for Machine Learning | Machine Learning Course10:13
- 5.3.3. Random Variables and its types | Discrete Random Variables | Continuous Random Variables09:41
- 5.3.4. Probability Distribution for Random Variable | Machine Learning Course10:07
- 5.3.5. Normal Distribution or Gaussian Distribution | Skewness | Probability for Machine Learning09:47
- 5.3.6. Poisson Distribution | Probability for Machine Learning10:38
- 6.1. What is a Machine Learning Model?21:07
- 6.2. Supervised Learning Models | Supervised Learning08:10
- 6.3. Unsupervised Learning Models | Unsupervised Learning06:54
- 6.4. How to choose the right Machine Learning Model | Model Selection | Cross Validation14:23
- 6.5. Overfitting in Machine Learning | Causes for Overfitting and its Prevention14:14
- 6.6. Underfitting in Machine Learning | Causes for Underfitting and its Prevention08:47
- 6.7. Bias Variance Tradeoff | Machine Learning18:49
- 6.8. Loss Function in Machine Learning14:19
- 6.9. Model Evaluation in Machine Learning | Accuracy score | Mean Squared Error15:48
- 6.10. Model Parameters and Hyperparameters | Weights & Bias | Learning Rate & Epochs32:43
- 6.11. Gradient Descent in Machine Learning26:16
- 7.1.1. Linear Regression - Intuition | Machine Learning Models29:24
- 7.1.2. Linear Regression - Mathematical Understanding20:50
- 7.1.3. Gradient Descent for Linear Regression19:06
- 7.1.4. Building Linear Regression from scratch in Python49:48
- 7.1.5. Implementing Linear Regression from scratch in Python01:04:09
- 7.2.1. Logistic Regression - Intuition | Machine Learning Course22:31
- 7.2.2. Math behind Logistic Regression | Machine Learning Models17:31
- 7.2.3. Loss Function and Cost Function for Logistic Regression29:13
- 7.2.4. Gradient Descent for Logistic Regression19:52
- 7.2.5. Building Logistic Regression from scratch in Python01:05:18
- 7.2.6. Implementing Logistic Regression from scratch in Python29:16
- Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation38:55
- Project 17. Spam Mail Prediction using Machine Learning with Python | Machine Learning Projects01:02:54
- Project 18. Movie Recommendation System using Machine Learning with Python01:15:03
- 7.3.1. Support Vector Machine Classifier - Intuition15:52
- 7.3.2. Math behind Support Vector Machine Classifier31:55
- 7.3.3. Support Vector Machine - Kernels19:52
- 7.3.4. Loss Function for Support Vector Machine Classifier - Hinge Loss22:18
- 7.3.5. Gradient Descent for Support Vector Machine Classifier18:28
- 7.3.6. Building Support Vector Machine Classifier from scratch in Python01:05:03
- 7.3.7. Implementing Support Vector Machine Classifier from Scratch in Python58:21
- Machine Learning - Interview Questions and Answers - Part 240:15
- Project 19. Breast Cancer Classification using Machine Learning | Machine Learning Projects58:55
- Anaconda and Streamlit installation for Machine Learning Model Deployment14:04
- Deploy Machine Learning Model using Streamlit in Python | ML model Deployment40:24
- 7.4.1. Lasso Regression - Intuition21:05
- 7.4.2. Math Behind Lasso Regression22:15
- 7.4.3. Gradient Descent for Lasso Regression18:40
- 7.4.4. Building Lasso Regression from Scratch in Python53:42
- 7.5.1. K-Nearest Neighbors (KNN) - intuition17:42
- 7.5.2. Math behind K-Nearest Neighbors (KNN)14:48
- 7.5.3. Calculating Euclidean and Manhattan distance in Python23:09
- 7.5.4. K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier50:15
- 7.5.5. Implementing K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier28:48
- 7.6.1. Decision tree - intuition17:16
- 7.6.2. Entropy, Information Gain & Gini Impurity - Decision Tree18:23
- K Fold Cross Validation | Cross Validation in Machine Learning17:06
- 8.2. Cross Validation - Python implementation | cross_val_score | Cross Validation in Sklearn47:20
- 8.3. Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV13:36
- 8.4. GridSearchCV and RandomizedSearchCV - Python implementation | Hyperparameter Tuning40:09
- DL Project 1. Breast Cancer Classification with Neural Network | Deep Learning Projects in Python01:21:07
- Processing Image data in Python for Deep Learning Applications | Image Processing with Python41:03
- DL Project 2. MNIST Digit Classification with Neural Network | Deep Learning Projects in Python01:30:45
- 8.5. Model Selection in Machine Learning | How to choose the right Machine Learning model15:35
- 8.6. Model Selection in Machine Learning with Python | Choosing the right Machine Learning model01:05:08
- 8.7. Accuracy Score and Confusion Matrix - Concept & Python implementation | Model Evaluation in ML32:01
- 8.8. Precision, Recall, F1 score | Model Evaluation32:12
- 8.9. Precision, Recall, F1 Score - Python Implementation | Model Evaluation in Machine Learning26:33
- How to Deploy Machine Learning Model as an API in Python - FastAPI43:25
- Deploying ML model as Public API using FastAPI and Ngrok in Google Colaboratory21:02
- Deploying Machine Learning model as API on Heroku | FastAPI | Heroku | Python | ML24:46
- Deploying a Machine Learning web app using Streamlit on Heroku18:21
- DL Project 3. Dog vs Cat Classification using Transfer Learning | Deep Learning Projects in Python01:28:27
- DL Project 4. CIFAR - 10 Object Recognition using ResNet50 | Deep Learning Projects in Python01:35:45
- DL Project 5. Face Mask Detection using Convolutional Neural Network (CNN) - Deep Learning Projects01:21:21
- Project 20. Rainfall Prediction Using Machine Learning | Complete ML Project Walkthrough01:40:30
- Project 21. Autism Prediction Using Machine Learning | Complete ML Project Walkthrough 🚀02:18:25
- Project 22. Customer Churn Prediction Using Machine Learning | Complete ML Project Walkthrough 🚀01:53:05
- House Price Prediction Using Machine Learning | Step-by-Step | ML Projects02:17:40
- Clustering Models Explained with Intuition (Handwritten) | K-Means, DBSCAN, Hierarchical45:24
