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 • 107 lessons • 79.3 hours of video
Machine Learning Course With Python
107 lessons
• 39.5 hours
Machine Learning Course With Python
107 lessons
• 39.5 hours
- Machine Learning Course curriculum | Machine Learning - Roadmap 09:24
- 1.1 AI vs Machine Learning vs Deep Learning | AI vs ML vs DL | Machine Learning Training with Python 05:35
- 1.2. Supervised vs Unsupervised vs Reinforcement Learning | Types of Machine Learning 07:04
- 1.3. Supervised Learning | Types of Supervised Learning | Machine Learning Tutorial 06:13
- 1.4. Unsupervised Learning | Clustering and Association Algorithms in Machine Learning 08:03
- 1.5. What is Deep Learning | Deep Learning Tutorial | Deep Learning Simplified 08:40
- 2.1. Google Colaboratory for Python | Getting started with Google Colaboratory | Google Colab basics 10:17
- 2.2. Python Basics | Python Tutorial For Beginners | Learn Python Programming from Scratch 23:04
- 2.3. Python Basic Data Types | Python Tutorial | int float string complex boolean 20:40
- 2.4. Python Special data types | List Tuple Set Dictionary | Python Tutorial 27:04
- 2.5. Operators in Python | Python Tutorial |Arithmetic Assignment Comparison Logical Identity Member 19:29
- 2.6. if else statement in Python | if else | if elif else | nested if statement | Python Tutorial 13:59
- 2.7. Loops in Python | For Loop in Python | While Loop in Python | Python Tutorial 15:50
- 2.8. Functions in Python | Python Tutorial for Beginners 15:12
- 3.1. Complete Numpy Tutorial in Python | Numpy Arrays 45:52
- 3.2. Complete Pandas Tutorial in Python | Pandas Dataframe Tutorial 47:04
- 3.3. Matplotlib Tutorial in Python | Machine Learning Course with Python 30:54
- 3.4. Seaborn Tutorial in Python | Machine Learning Course 35:56
- 4.1. Where to Collect Data For Machine Learning? | Data Collection 13:26
- 4.2. Importing Datasets through Kaggle API 14:29
- 4.3. Handling Missing Values in Machine Learning | Imputation | Dropping 21:59
- 4.4. Data Standardization | Data Preprocessing | Machine Learning Course 20:14
- 4.5. Label Encoding | Data Pre-Processing | Machine Learning Course 19:18
- 4.6. Train Test Split | Splitting the dataset to Training and Testing data | Machine Learning Course 12:32
- 4.7. How to Handle imbalanced Dataset | Data Pre-Processing | Machine Learning Course 19:10
- 4.8. Feature extraction of Text data using Tfidf Vectorizer | Data Preprocessing | Machine Learning 11:58
- 4.9. Numerical Dataset Pre-Processing - Use Case | Machine Learning Course with Python 20:35
- 4.10. Text Dataset Pre-Processing - Use Case | Machine Learning Course | Data Pre Processing 36:21
- 5.0. Mathematics for Machine Learning - Introduction | Machine Learning Course 06:05
- 5.1.1. Linear Algebra - Vectors | Mathematics for Machine Learning 10:19
- 5.1.2. Vector Operations - Part 1 | Mathematics for Machine Learning | Linear Algebra 13:43
- 5.1.3. Vector Operations - in Python - Part 1 | Math for Machine Learning | Linear Algebra 19:53
- 5.1.4. Vector Operations - Part 2 | Dot Product | Cross Product | Projection of vector | Math for ML 10:24
- 5.1.5. Vector Operations - in Python - Part 2 | Dot Product | Cross Product | Projection of vector 18:59
- 5.1.6. Matrix - Basics | Math for Machine Learning | Linear Algebra 14:58
- 5.1.7. Working with Matrix in Python | Mathematics for Machine Learning | Linear Algebra 19:28
- 5.1.8. Matrix Operations - Addition, Subtraction, Multiplication | Mathematics for Machine Learning 19:56
- 5.1.9. Matrix Operations in Python | Mathematics for Machine Learning | Linear Algebra 32:25
- 5.2.1. Statistics for Machine Learning | Machine Learning course 08:58
- 5.2.2. Basics of Statistics | Types of Data in Statistics | Statistics for Machine Learning 13:40
- 5.2.3. Types of Statistics | Descriptive and Inferential Statistics | Machine Learning Course 14:05
- 5.2.4. Types of statistical studies | Statistics for Machine Learning | Machine Learning course 12:24
- 5.2.5. Population and Sample | Sampling techniques | Statistics for Machine Learning 23:38
- 5.2.6. Measure of Central Tendencies - Mean, Median, Mode | Statistics for Machine Learning 16:32
- 5.2.7. Measure of Variability - Range, Variance, Standard Deviation | Math for Machine Learning 12:54
- 5.2.8. Percentiles and Quantiles | Statistics for Machine Learning | Machine Learning Course 08:56
- 5.2.9. Correlation and Causation | Statistics for machine learning | Machine Learning Course 13:43
- 5.2.10. Hypothesis Testing | Null Hypothesis and Alternative Hypothesis | Math For Machine Learning 10:12
- 5.3.1. Probability for Machine Learning | Machine Learning Course 08:27
- 5.3.2. Basics of Probability | Probability for Machine Learning | Machine Learning Course 10:13
- 5.3.3. Random Variables and its types | Discrete Random Variables | Continuous Random Variables 09:41
- 5.3.4. Probability Distribution for Random Variable | Machine Learning Course 10:07
- 5.3.5. Normal Distribution or Gaussian Distribution | Skewness | Probability for Machine Learning 09:47
- 5.3.6. Poisson Distribution | Probability for Machine Learning 10:38
- 6.1. What is a Machine Learning Model? 21:07
- 6.2. Supervised Learning Models | Supervised Learning 08:10
- 6.3. Unsupervised Learning Models | Unsupervised Learning 06:54
- 6.4. How to choose the right Machine Learning Model | Model Selection | Cross Validation 14:23
- 6.5. Overfitting in Machine Learning | Causes for Overfitting and its Prevention 14:14
- 6.6. Underfitting in Machine Learning | Causes for Underfitting and its Prevention 08:47
- 6.7. Bias Variance Tradeoff | Machine Learning 18:49
- 6.8. Loss Function in Machine Learning 14:19
- 6.9. Model Evaluation in Machine Learning | Accuracy score | Mean Squared Error 15:48
- 6.10. Model Parameters and Hyperparameters | Weights & Bias | Learning Rate & Epochs 32:43
- 6.11. Gradient Descent in Machine Learning 26:16
- 7.1.1. Linear Regression - Intuition | Machine Learning Models 29:24
- 7.1.2. Linear Regression - Mathematical Understanding 20:50
- 7.1.3. Gradient Descent for Linear Regression 19:06
- 7.1.4. Building Linear Regression from scratch in Python 49:48
- 7.1.5. Implementing Linear Regression from scratch in Python 01:04:09
- 7.2.1. Logistic Regression - Intuition | Machine Learning Course 22:31
- 7.2.2. Math behind Logistic Regression | Machine Learning Models 17:31
- 7.2.3. Loss Function and Cost Function for Logistic Regression 29:13
- 7.2.4. Gradient Descent for Logistic Regression 19:52
- 7.2.5. Building Logistic Regression from scratch in Python 01:05:18
- 7.2.6. Implementing Logistic Regression from scratch in Python 29:16
- Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation 38:55
- 7.3.1. Support Vector Machine Classifier - Intuition 15:52
- 7.3.2. Math behind Support Vector Machine Classifier 31:55
- 7.3.3. Support Vector Machine - Kernels 19:52
- 7.3.4. Loss Function for Support Vector Machine Classifier - Hinge Loss 22:18
- 7.3.5. Gradient Descent for Support Vector Machine Classifier 18:28
- 7.3.6. Building Support Vector Machine Classifier from scratch in Python 01:05:03
- 7.3.7. Implementing Support Vector Machine Classifier from Scratch in Python 58:21
- Machine Learning - Interview Questions and Answers - Part 2 40:15
- Anaconda and Streamlit installation for Machine Learning Model Deployment 14:04
- 7.4.1. Lasso Regression - Intuition 21:05
- 7.4.2. Math Behind Lasso Regression 22:15
- 7.4.3. Gradient Descent for Lasso Regression 18:40
- 7.4.4. Building Lasso Regression from Scratch in Python 53:42
- 7.5.1. K-Nearest Neighbors (KNN) - intuition 17:42
- 7.5.2. Math behind K-Nearest Neighbors (KNN) 14:48
- 7.5.3. Calculating Euclidean and Manhattan distance in Python 23:09
- 7.5.4. K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier 50:15
- 7.5.5. Implementing K-Nearest Neighbors Classifier from Scratch in Python | KNN Classifier 28:48
- 7.6.1. Decision tree - intuition 17:16
- 7.6.2. Entropy, Information Gain & Gini Impurity - Decision Tree 18:23
- K Fold Cross Validation | Cross Validation in Machine Learning 17:06
- 8.2. Cross Validation - Python implementation | cross_val_score | Cross Validation in Sklearn 47:20
- 8.3. Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV 13:36
- 8.4. GridSearchCV and RandomizedSearchCV - Python implementation | Hyperparameter Tuning 40:09
- 8.5. Model Selection in Machine Learning | How to choose the right Machine Learning model 15:35
- 8.6. Model Selection in Machine Learning with Python | Choosing the right Machine Learning model 01:05:08
- 8.7. Accuracy Score and Confusion Matrix - Concept & Python implementation | Model Evaluation in ML 32:01
- 8.8. Precision, Recall, F1 score | Model Evaluation 32:12
- 8.9. Precision, Recall, F1 Score - Python Implementation | Model Evaluation in Machine Learning 26:33
- Clustering Models Explained with Intuition (Handwritten) | K-Means, DBSCAN, Hierarchical 45:24
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