Machine Learning And Deep Learning In One Semester

Machine Learning And Deep Learning In One Semester - Printable Version

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Machine Learning And Deep Learning In One Semester - SKIKDA - 08-24-2023

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Published 8/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.17 GB | Duration: 46h 45m

Practical Oriented Explanations by solving more than 80 projects with Numpy, Scikit-learn, Pandas, Matplotlib, Pytorch.

What you'll learn
Theory, Maths and Implementation of machine learning and deep learning algorithms.
Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest
Build Artificial Neural Networks and use them for Regression and Classification Problems
Using GPU with Neural Networks and Deep Learning Models.
Convolutional Neural Networks
Transfer Learning
Recurrent Neural Networks and LSTM
Time series forecasting and classification.
Autoencoders
Generative Adversarial Networks (GANs)
Python from scratch
Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries.
More than 80 projects solved with Machine Learning and Deep Learning models

Requirements
Some Programming Knowledge is preferable but not necessary
Gmail account ( For Google Colab )

Description
IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrincipal Component Analysis (PCA)What you'll learnTheory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scratchNumpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.More than 80 projects solved with Machine Learning and Deep Learning models.Who this course is for:Students in Machine Learning and Deep Learning course.Beginners Who want to Learn Machine Learning and Deep Learning from Scratch.Researchers in Artificial Intelligence.Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks.Want to switch from Matlab and Other Programming Languages to Python.

Overview
Section 1: Introduction and Course Material

Lecture 1 Introduction of the course

Lecture 2 Course Material

Section 2: Introduction to Machine Learning and Deep Learning

Lecture 3 Introduction of the Section

Lecture 4 What in Intelligence?

Lecture 5 Machine Learning

Lecture 6 Supervised Machine Learning

Lecture 7 Unsupervised Machine Learning

Lecture 8 Deep Learning

Section 3: Introduction to Google Colab

Lecture 9 Introduction of the Section

Lecture 10 Importing Dataset in Google Colab

Lecture 11 Importing and Displaying Image in Google Colab

Lecture 12 Importing more datasets

Lecture 13 Uploading Course Material on your Google Drive

Section 4: Python Crash Course

Lecture 14 Introduction of the Section

Lecture 15 Arithmetic With Python

Lecture 16 Comparison and Logical Operations

Lecture 17 Conditional Statements

Lecture 18 Dealing With Numpy Arrays-Part01

Lecture 19 Dealing With Numpy Arrays-Part02

Lecture 20 Dealing With Numpy Arrays-Part03

Lecture 21 Plotting and Visualization-Part01

Lecture 22 Plotting and Visualization-Part02

Lecture 23 Plotting and Visualization-Part03

Lecture 24 Plotting and Visualization-Part04

Lecture 25 Lists in Python

Lecture 26 For Loops-Part01

Lecture 27 For Loops-Part02

Lecture 28 Strings

Lecture 29 Print Formatting With Strings

Lecture 30 Dictionaries-Part01

Lecture 31 Dictionaries-Part02

Lecture 32 Functions in Python-Part01

Lecture 33 Functions in Python-Part02

Lecture 34 Pandas-Part01

Lecture 35 Pandas-Part02

Lecture 36 Pandas-Part03

Lecture 37 Pandas-Part04

Lecture 38 Seaborn-Part01

Lecture 39 Seaborn-Part02

Lecture 40 Seaborn-Part03

Lecture 41 Tuples

Lecture 42 Classes in Python

Section 5: Data Preprocessing

Lecture 43 Introduction of the Section

Lecture 44 Need of Data Preprocessing

Lecture 45 Data Normalization and Min-Max Scaling

Lecture 46 Project01-Data Normalization and Min-Max Scaling-Part01

Lecture 47 Project01-Data Normalization and Min-Max Scaling-Part02

Lecture 48 Data Standardization

Lecture 49 Project02-Data Standardization

Lecture 50 Project03-Dealing With Missing Values

Lecture 51 Project04-Dealing With Categorical Features

Lecture 52 Project05-Feature Engineering

Lecture 53 Project06-Feature Engineering by Window Method

Section 6: Supervised Machine Learning

Lecture 54 Supervised Machine Learning

Section 7: Regression Analysis

Lecture 55 Introduction of the Section

Lecture 56 Origin of the Regression

Lecture 57 Definition of Regression

Lecture 58 Requirement from Regression

Lecture 59 Simple Linear Regression

Lecture 60 Multiple Linear Regression

Lecture 61 Target and Predicted Values

Lecture 62 Loss Function

Lecture 63 Regression With Least Square Method

Lecture 64 Least Square Method With Numerical Example

Lecture 65 Evaluation Metrics for Regression

Lecture 66 Project01-Simple Regression-Part01

Lecture 67 Project01-Simple Regression-Part02

Lecture 68 Project01-Simple Regression-Part03

Lecture 69 Project02-Multiple Regression-Part01

Lecture 70 Project02-Multiple Regression-Part02

Lecture 71 Project02-Multiple Regression-Part03

Lecture 72 Project03-Another Multiple Regression

Lecture 73 Regression by Gradient Descent

Lecture 74 Project04-Simple Regression With Gradient Descent

Lecture 75 Project05-Multiple Regression With Gradient Descent

Lecture 76 Polynomial Regression

Lecture 77 Project06-Polynomial Regression

Lecture 78 Cross-validation

Lecture 79 Project07-Cross-validation

Lecture 80 Underfitting and Overfitting ( Bias-Variance Tradeoff )

Lecture 81 Concept of Regularization

Lecture 82 Ridge Regression OR L2 Regularization

Lecture 83 Lasso Regression OR L1 Regularization

Lecture 84 Comparing Ridge and Lasso Regression

Lecture 85 Elastic Net Regularization

Lecture 86 Project08-Regularizations

Lecture 87 Grid search Cross-validation

Lecture 88 Project09-Grid Search Cross-validation

Section 8: Logistic Regression

Lecture 89 Introduction of the Section

Lecture 90 Fundamentals of Logistic Regression

Lecture 91 Limitations of Regression Models

Lecture 92 Transforming Linear Regression into Logistic Regression

Lecture 93 Project01-Getting Class Probabilities-Part01

Lecture 94 Project01-Getting Class Probabilities-Part02

Lecture 95 Loss Function

Lecture 96 Model Evaluation-Confusion Matrix

Lecture 97 Accuracy, Precision, Recall and F1-Score

Lecture 98 ROC Curves and Area Under ROC

Lecture 99 Project02-Evaluating Logistic Regression Model

Lecture 100 Project03-Cross-validation With Logistic Regression Model

Lecture 101 Project04-Multiclass Classification

Lecture 102 Project05-Classification With Challenging Dataset-Part01

Lecture 103 Project05-Classification With Challenging Dataset-Part02

Lecture 104 Project05-Classification With Challenging Dataset-Part03

Lecture 105 Grid Search Cross-validation With Logistic Regression

Section 9: K-Nearest Neighbors ( KNN )

Lecture 106 Introduction of the Section

Lecture 107 Intuition Behind KNN

Lecture 108 Steps of KNN Algorithm

Lecture 109 Numerical Example on KNN Algorithm

Lecture 110 Project01-KNN Algorithm-Part01

Lecture 111 Project01-KNN Algorithm-Part02

Lecture 112 Finding Optimal Value of K

Lecture 113 Project02-Implementing KNN

Lecture 114 Project03-Implementing KNN

Lecture 115 Project04-Implementing KNN

Lecture 116 Advantages and disadvantages of KNN

Section 10: Bayes Theorem and Naive Bayes Classifier

Lecture 117 Introduction of the section

Lecture 118 Fundamentals of Probability

Lecture 119 Conditional Probability and Bayes Theorem

Lecture 120 Numerical Example on Bayes Theorem

Lecture 121 Naive Bayes Classification

Lecture 122 Comparing Naive Bayes Classification With Logistic Regression

Lecture 123 Project01_Naive Bayes as probabilistic classifier

Lecture 124 Project02_Comparing Naive Bayes and Logistic Regression

Lecture 125 Project03_Multiclass Classification With Naive Bayes Classifier

Section 11: Support Vector Machines ( SVM )

Lecture 126 Introduction of the Section

Lecture 127 Basic Concept of SVM

Lecture 128 Maths of SVM

Lecture 129 Hard and Soft Margin Classifier

Lecture 130 Decision rules of SVM

Lecture 131 Kernel trick in SVM

Lecture 132 Project01-Understanding SVM-Part01

Lecture 133 Project01-Understanding SVM-Part02

Lecture 134 Project02-Multiclass Classification With SVM

Lecture 135 Project03-Grid Search CV-Part01

Lecture 136 Project03-Grid Search CV-Part02

Lecture 137 Project04-Breast Cancer Classification with SVM

Section 12: Decision Tree

Lecture 138 Introduction of the Section

Lecture 139 Concept of Decision Tree

Lecture 140 Important terms related to decision tree

Lecture 141 Entropy-An information gain criterion

Lecture 142 Numerical Example on Entropy-Part01

Lecture 143 Numerical Example on Entropy-Part02

Lecture 144 Gini Impurity - An information criterion

Lecture 145 Numerical Example on Gini Impurity

Lecture 146 Project01-Decision Tree Implementation

Lecture 147 Project02-Breast Cancer Classification With Decision Tree

Lecture 148 Project03-Grid Search CV with Decision Tree

Section 13: Random Forest

Lecture 149 Introduction of the Section

Lecture 150 Why Random Forest

Lecture 151 Working of Random Forest

Lecture 152 Hyperparameters of Random Forest

Lecture 153 Bootstrap sampling and OOB Error

Lecture 154 Project01-Random Forest-Part01

Lecture 155 Project01-Random Forest-Part02

Lecture 156 Project02-Random Forest-Part01

Lecture 157 Project02-Random Forest-Part02

Section 14: Boosting Methods in Machine Learning

Lecture 158 Introduction of the Section

Lecture 159 AdaBoost (Adaptive Boosting )

Lecture 160 Numerical Example on Adaboost

Lecture 161 Project01-AdaBoost Classifier

Lecture 162 Project02-AdaBoost Classifier

Lecture 163 Gradient Boosting

Lecture 164 Numerical Example on Gradient Boosting

Lecture 165 Project03-Gradient Boosting

Lecture 166 Project04-Gradient Boosting

Lecture 167 Extreme Gradient Boosting ( XGBoost )

Lecture 168 Project05-XGBoost-Part01

Lecture 169 Project05-XGBoost-Part02

Section 15: Deep Learning

Lecture 170 Deep Learning

Section 16: Introduction to Neural Networks and Deep Learning

Lecture 171 Introduction of the Section

Lecture 172 The perceptron

Lecture 173 Features, Weights and Activation Function

Lecture 174 Learning of Neural Network

Lecture 175 Rise of Deep Learning

Section 17: Activation Functions

Lecture 176 Introduction of the Section

Lecture 177 Classification by Perceptron-Part01

Lecture 178 Classification by Perceptron-Part02

Lecture 179 Need of Activation Functions

Lecture 180 Adding Activation Function to Neural Network

Lecture 181 Sigmoid as Activation Function

Lecture 182 Hyperbolic Tangent Function

Lecture 183 ReLU and Leaky ReLU Function

Students in Machine Learning and Deep Learning course,Beginners Who want to Learn Machine Learning and Deep Learning from Scratch,Researchers in Artificial Intelligence,Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks,Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning

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