What you'll learnTheory, 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
RequirementsSome Programming Knowledge is preferable but not necessary
Gmail account ( For Google Colab )
DescriptionIntroductionIntroduction 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.
OverviewSection 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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