Machine Learning Essentials (2023)

سرفصل های دوره

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

1. Introduction

1. Course Overview

2. Artificial Intelligence

3. Machine Learning

4. Deep Learning

5. Computer Vision

6. Natural Language Processing

7. Automatic Speech Recognition

8. Reinforcement Learning

9. Pre-requisites.html

10. Code Repository.html

2. Supervised vs Unsupervised Learning

1. Supervised Learning Introduction

2. Supervised Learning Example

3. Unsupervised Learning

3. Linear Regression

1. Introduction to Linear Regression

2. Notation

3. Hypothesis

4. Loss Error Function

5. Training Idea

6. Gradient Descent Optimisation

7. Gradient Descent Code

8. Gradient Descent - for Linear Regression

9. The Math of Training

10. Code 01 - Data Generation

11. Code 02 - Data Normalisation

12. Code 03 - Train Test Split

13. Code 04 - Modelling

14. Code 05 - Predictions

15. R2 Score

16. Code 06 - Evaluation

17. Code 07 - Visualisation

18. Code 08 - Trajectory [Optional]

4. Linear Regression - Multiple Features

1. Introduction

2. Hypothesis

3. Loss Function

4. Training & Gradient Updates

5. Code 01 - Data Prep

6. Code 02 - Hypothesis

7. Code 03 - Loss Function

8. Code 04 - Gradient Computation

9. Code 05 - Training Loop

10. A Note about Shapes

11. Code 06 - Evaluation

12. Linear Regression using Sk-Learn

5. Logistic Regression

1. Binary Classification Introduction

2. Notation

3. Hypothesis Function

4. Binary Cross-Entropy Loss Function

5. Gradient Update Rule

6. Code 01 - Data Prep

7. Code 02 - Hypothesis Logit Model

8. Code 03 - Binary Cross Entropy Loss

9. Code 04 - Gradient Computation

10. Code 05 - Training Loop

11. Code 06 - Visualise Decision Boundary

12. Code 07 - Predictions & Accuracy

13. Logistic Regression using Sk-Learn

14. Multiclass Classification One Vs Rest

15. Multiclass Classification One Vs One

6. Dimensionality Reduction Feature Selection

1. Curse of Dimensionality

2. Feature Selection Vs. Feature Extraction

3. Filter Method

4. Wrapper Method

5. Embedded Method

6.1 train.csv

6. Feature Selection - Code

7. Principal Component Analysis (PCA)

1. Introduction to PCA

2. Conceptual Overview of PCA

3. Maximising Variance

4. Minimising Distances

5. Eigen Values & Eigen Vectors

6. PCA Summary

7. Understanding Eigen Values

8. PCA Code

9. Choosing the right dimensions

8. K-Nearest Neigbours

1. Introduction

2. KNN Idea

3. KNN Data Prep

4. KNN Algorithm Code

5. Euclidean and Manhattan Distance

6. Deciding value of K

7. KNN and Data Standardisation

8. KNN Pros and Cons

9. KNN using Sk-Learn.html

9. PROJECT - Face Recognition

1. OpenCV - Working with Images

2. OpenCV - Video Input from WebCam

3. Object Detection using Haarcascades

4. Face Detection in Images

5. Face Detection in Live Video

6. Face Recognition Project Intro

7. Face Recognition 01 - Data Collection

8. Face Recognition 02 - Loading Data

9. Face Recognition 03 - Predictions using KNN

10. K-Means

1. K-Means Algorithm

2. Code 01 - Data Prep

3. Code 02 - Init Centers

4. Code 03 - Assigning Points

5. Code 04 - Updating Centroids

6. Code 05 - Visualizing K-Means & Results

11. Project - Dominant Color Extraction

1. Introduction

2. Reading Images

3. Finding Clusters

4. Dominant Color Swatches

5. Image in K-Colors

12. Naive Bayes Algorithm

1. Bayes Theorem

2. Derivation of Bayes Theorem

3. Bayes Theorem Question

4. Naive Bayes Algorithm

5. Naive Bayes for Text Classification

6. Computing Likelihood

7.1 golf.csv

7. Understanding Golf Dataset

8. CODE - Prior Probability

9. CODE - Conditional Probability

10. CODE - Likelihood

11. CODE - Prediction

12. Implementing Naive Bayes - Sklearn

13. Multinomial Naive Bayes

1. Multinomial Naive Bayes

2. Laplace Smoothing

3. Multinomial Naive Bayes Example

4. Bernoulli Naive Bayes

5. Bernoulli Naive Bayes Example

6. Bias Variance Tradeoff

7. Gaussian Naive Bayes

8. CODE - Variants of Naive Bayes

14. PROJECT Spam Classifier

1. Project Overview

2. Data Clearning

3. WordCloud

4. Text Featurization

5. Model Building

6. Model Evaluation

15. Decision Trees

1. Decision Trees Introduction

2. Decision Trees Example

3. Entropy

4. CODE Entropy

5. Information Gain

6. CODE Split Data

7. CODE Information Gain

8. Construction of Decision Trees

9. Stopping Conditions

16. Decision Trees Implementation

1. CODE - Decision Tree Node

2. CODE - Train Decision Tree

3. CODE - Assign Target Variable to Each Node

4. CODE - Stopping Conditions

5. CODE - Train Child Nodes

6. CODE - Explore Decision Tree Model

7. CODE - Prediction

8. Handling Numeric Features

9. Bias Variance Tradeoff

10. Decision Trees for Regression

11. Decision Tree Code - Sklearn

17. PROJECT Titanic Survival Prediction

1.1 titanic train.csv

1. Project Overview

2. Exploratory Data Analysis

3. Exploratory Data Analysis - II

4. Data Preparation for ML Model

5. Handling Missing Values

6. Decision Tree Model Building

7. Visualize Decision Tree

18. Ensemble Learning Bagging

1. Ensemble Learning

2. Bagging Model

3. Why Bagging Helps

4. Random Forest Algorithm

5. Bias Variance Tradeoff

6. CODE Random Forest

19. Ensemble Learning Boosting

1. Boosting Introduction

2. Boosting Intuition

3. Boosting Mathematical Formulation

4. Concept of Pseudo Residuals

5. GBDT Algorithm

6. Bias Variance Tradeoff

7. CODE - Gradient Boosting Decision Trees

8. XGBoost

9. Adaptive Boosting (AdaBoost)

20. PROJECT Customer Churn Prediction

1. Project Overview

2. Exploratory Data Analysis

3. Data Visualisation

4. Finding relations

5. Data Preparation

6. Model Building

7. Hyperparameter tuning

21. Deep Learning Introduction - Neural Network

1. Biological Neural Network

2. A Neuron

3. How does a perceptron Learns

4. Gradient Descent Updates

5. Neural Networks

6. 3 Layer NN

7. Why Neural Nets

8. Tensorflow Playground

9. CODE -Data Preparation

10. CODE - Model Building

11. CODE - Model Training and Testing

22. PROJECT Pokemon Image Classification

1.1 Dataset Link.html

1. Introduction

2. The Data

3. Structured Data

4. Data Loading

5. Data Preprocessing

6. Model Architecture

7. Softmax Function

8. Model Training

9. Model evaluation

10. Predictions

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