Mastering Machine Learning Algorithms using Python

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Mastering Machine Learning Algorithms using Python

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收录时间：2024-08-05
最近下载：2024-12-25

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文件列表

  • Chapter 13 Introduction to Deep Learning/001. Introduction to Deep Learning.mp4 262.8 MB
  • Chapter 04 Exploratory Data Analysis/009. EDA Project 7.mp4 138.3 MB
  • Chapter 03 Learning Python/010. Python Sets 1.mp4 136.7 MB
  • Chapter 06 Logistic Regression/006. Model Evaluation - AUC-ROC.mp4 127.3 MB
  • Chapter 03 Learning Python/023. Pandas DataFrame 5.mp4 126.1 MB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/002. K-Means Clustering Computation.mp4 120.4 MB
  • Chapter 01 Introduction to Machine Learning/004. History of Machine Learning.mp4 115.6 MB
  • Chapter 06 Logistic Regression/004. Data Analysis and Feature Engineering.mp4 108.3 MB
  • Chapter 05 Linear Regression/010. Data Preparation and Analysis 3.mp4 104.6 MB
  • Chapter 02 Statistical Techniques/008. Hypothesis Testing.mp4 100.4 MB
  • Chapter 03 Learning Python/016. Pandas Series 2.mp4 98.3 MB
  • Chapter 06 Logistic Regression/002. Logit Model.mp4 96.7 MB
  • Chapter 01 Introduction to Machine Learning/007. Challenges in Machine Learning.mp4 95.0 MB
  • Chapter 02 Statistical Techniques/004. Histograms and Normal Approximation.mp4 93.4 MB
  • Chapter 09 Random Forest Ensemble/003. Model Building and Hyperparameter Tuning using Grid Search CV.mp4 92.2 MB
  • Chapter 05 Linear Regression/006. OLS Assumptions and Testing.mp4 91.4 MB
  • Chapter 03 Learning Python/007. Python Tuples.mp4 90.3 MB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/001. Principal Component Analysis - Concepts.mp4 90.0 MB
  • Chapter 08 Decision Tree Classifier/006. Model Optimization using Grid Search Cross Validation.mp4 89.7 MB
  • Chapter 03 Learning Python/003. Python Variables and Conditions.mp4 88.6 MB
  • Chapter 05 Linear Regression/011. Model Building.mp4 86.6 MB
  • Chapter 03 Learning Python/008. Python Dictionaries 1.mp4 86.3 MB
  • Chapter 08 Decision Tree Classifier/002. Decision Tree - Learning Steps.mp4 86.0 MB
  • Chapter 06 Logistic Regression/005. Build the Logistic Model.mp4 85.2 MB
  • Chapter 02 Statistical Techniques/007. Binomial Theory - Expected Value and Standard Error.mp4 84.6 MB
  • Chapter 05 Linear Regression/002. Training and Cost Function.mp4 84.4 MB
  • Chapter 04 Exploratory Data Analysis/003. EDA Project 1.mp4 83.6 MB
  • Chapter 03 Learning Python/017. Pandas Series 3.mp4 83.5 MB
  • Chapter 06 Logistic Regression/003. Telecom Churn Case Study.mp4 82.2 MB
  • Chapter 03 Learning Python/026. Python Lambda Functions.mp4 82.2 MB
  • Chapter 04 Exploratory Data Analysis/008. EDA Project 6.mp4 81.6 MB
  • Chapter 09 Random Forest Ensemble/002. Random Forest Steps Pruning and Optimization.mp4 81.3 MB
  • Chapter 06 Logistic Regression/001. Logistic Regression Introduction.mp4 80.9 MB
  • Chapter 01 Introduction to Machine Learning/008. Machine Learning Life Cycle and Pipelines.mp4 80.6 MB
  • Chapter 03 Learning Python/006. Python Lists.mp4 80.0 MB
  • Chapter 02 Statistical Techniques/005. Central Limit Theorem.mp4 79.4 MB
  • Chapter 05 Linear Regression/012. Model Evaluation and Optimization.mp4 79.2 MB
  • Chapter 01 Introduction to Machine Learning/012. Optimizing Classification Metrics.mp4 77.2 MB
  • Chapter 03 Learning Python/018. Pandas Series 4.mp4 76.9 MB
  • Chapter 04 Exploratory Data Analysis/007. EDA Project 5.mp4 75.5 MB
  • Chapter 05 Linear Regression/007. Car Price Prediction.mp4 75.4 MB
  • Chapter 07 Naive Bayes Classification Algorithm/003. Employee Attrition Case Study.mp4 75.1 MB
  • Chapter 01 Introduction to Machine Learning/005. Machine Learning Use Cases and Types.mp4 74.6 MB
  • Chapter 10 Support Vector Machine/001. Support Vector Machine Concepts.mp4 74.0 MB
  • Chapter 08 Decision Tree Classifier/005. Iris Dataset Case Study.mp4 73.7 MB
  • Chapter 03 Learning Python/015. Pandas Series 1.mp4 73.3 MB
  • Chapter 03 Learning Python/021. Pandas DataFrame 3.mp4 72.6 MB
  • Chapter 07 Naive Bayes Classification Algorithm/002. Naive Bayes Probability Computation.mp4 71.6 MB
  • Chapter 05 Linear Regression/003. Cost Functions and Gradient Descent.mp4 71.5 MB
  • Chapter 06 Logistic Regression/007. Model Optimization 1.mp4 70.7 MB
  • Chapter 04 Exploratory Data Analysis/002. Tools and Processes of EDA.mp4 70.6 MB
  • Chapter 03 Learning Python/024. Pandas DataFrame 6.mp4 70.3 MB
  • Chapter 03 Learning Python/013. Numpy Arrays 2.mp4 68.9 MB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/004. K-Means - Data Preparation and Modelling.mp4 68.9 MB
  • Chapter 02 Statistical Techniques/002. Types of Data and Descriptive Statistics.mp4 68.6 MB
  • Chapter 04 Exploratory Data Analysis/006. EDA Project 4.mp4 68.2 MB
  • Chapter 02 Statistical Techniques/006. Probability Theory.mp4 68.0 MB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/001. Unsupervised Learning - K-Mean Clustering.mp4 67.9 MB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/002. Principal Component Analysis - Computations 1.mp4 67.9 MB
  • Chapter 02 Statistical Techniques/001. Statistics and Experiments.mp4 67.0 MB
  • Chapter 03 Learning Python/012. Numpy Arrays 1.mp4 66.7 MB
  • Chapter 03 Learning Python/027. Python Lambda Functions and Date-Time Operations.mp4 66.7 MB
  • Chapter 10 Support Vector Machine/002. Support Vector Machine Metrics and Polynomial SVM.mp4 66.5 MB
  • Chapter 06 Logistic Regression/008. Model Optimization 2.mp4 64.8 MB
  • Chapter 03 Learning Python/019. Pandas DataFrame 1.mp4 64.2 MB
  • Chapter 08 Decision Tree Classifier/003. Gini Index and Entropy Measures.mp4 64.1 MB
  • Chapter 04 Exploratory Data Analysis/004. EDA Project 2.mp4 64.0 MB
  • Chapter 07 Naive Bayes Classification Algorithm/001. Naive Bayes Probability Model.mp4 63.8 MB
  • Chapter 09 Random Forest Ensemble/001. Ensemble Techniques Bagging and Random Forest.mp4 62.6 MB
  • Chapter 03 Learning Python/020. Pandas DataFrame 2.mp4 61.3 MB
  • Chapter 05 Linear Regression/004. Linear Regression - Practical Approach.mp4 61.0 MB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/004. Principal Component Analysis Practicals.mp4 60.8 MB
  • Chapter 03 Learning Python/022. Pandas DataFrame 4.mp4 60.4 MB
  • Chapter 07 Naive Bayes Classification Algorithm/004. Model Building and Optimization.mp4 58.0 MB
  • Chapter 01 Introduction to Machine Learning/001. Course Introduction.mp4 57.6 MB
  • Chapter 04 Exploratory Data Analysis/001. Exploratory Data Analysis.mp4 57.6 MB
  • Chapter 05 Linear Regression/009. Data Preparation and Analysis 2.mp4 57.5 MB
  • Chapter 05 Linear Regression/005. Feature Scaling and Cost Functions.mp4 55.9 MB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/005. K-Means - Model Optimization.mp4 55.8 MB
  • Chapter 09 Random Forest Ensemble/004. Optimization Continued.mp4 55.8 MB
  • Chapter 03 Learning Python/014. Numpy Arrays 3.mp4 55.7 MB
  • Chapter 08 Decision Tree Classifier/001. Decision Tree - Model Concept.mp4 55.1 MB
  • Chapter 01 Introduction to Machine Learning/010. Regression Models and Performance Metrics.mp4 54.5 MB
  • Chapter 05 Linear Regression/001. Linear Regression Introduction.mp4 54.1 MB
  • Chapter 03 Learning Python/025. Python User Defined Functions.mp4 52.7 MB
  • Chapter 01 Introduction to Machine Learning/011. Classification Problems and Performance Metrics.mp4 51.3 MB
  • Chapter 05 Linear Regression/008. Data Preparation and Analysis 1.mp4 50.7 MB
  • Chapter 10 Support Vector Machine/003. Support Vector Machine Project 1.mp4 50.7 MB
  • Chapter 08 Decision Tree Classifier/004. Pruning and Hyperparameter Tuning.mp4 50.6 MB
  • Chapter 03 Learning Python/004. Python Iterations 1.mp4 49.7 MB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/003. Principal Component Analysis - Computations 2.mp4 49.0 MB
  • Chapter 03 Learning Python/028. Python String Operations.mp4 46.9 MB
  • Chapter 01 Introduction to Machine Learning/002. Introduction to Machine Learning.mp4 45.6 MB
  • Chapter 10 Support Vector Machine/005. Support Vector Machine - Classifying Polynomial Data.mp4 45.0 MB
  • Chapter 03 Learning Python/002. Starting with Python with Jupyter Notebook.mp4 43.5 MB
  • Chapter 01 Introduction to Machine Learning/003. Machine Learning Terminology.mp4 42.8 MB
  • Chapter 01 Introduction to Machine Learning/009. Regression Problems.mp4 40.8 MB
  • Chapter 03 Learning Python/005. Python Iterations 2.mp4 39.8 MB
  • Chapter 01 Introduction to Machine Learning/013. Bias and Variance.mp4 37.2 MB
  • Chapter 03 Learning Python/009. Python Dictionaries 2.mp4 32.8 MB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/loan.csv 32.8 MB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/003. K-Means Clustering Optimization.mp4 32.3 MB
  • Chapter 04 Exploratory Data Analysis/005. EDA Project 3.mp4 31.1 MB
  • Chapter 01 Introduction to Machine Learning/006. Role of Data in Machine Learning.mp4 28.2 MB
  • Chapter 02 Statistical Techniques/003. Random Variables and Normal Distribution.mp4 27.2 MB
  • Chapter 03 Learning Python/001. Introduction to Python.mp4 26.8 MB
  • Chapter 10 Support Vector Machine/004. Support Vector Machine Predictions.mp4 22.8 MB
  • Chapter 05 Linear Regression/013. Model Optimization.mp4 14.1 MB
  • Chapter 03 Learning Python/011. Python Sets 2.mp4 10.3 MB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/credit-card-default.csv 2.9 MB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Machine-Learning-Foundations.ipynb 828.5 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Lending-Club-EDA-Project.ipynb 816.2 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/telecom-churn-prediction-logistic-regression.ipynb 801.0 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Python-Intro-Numpy-Pandas.ipynb 689.3 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/customer-segmentation-k-means-analysis.ipynb 560.4 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/churn_data.csv 484.4 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Decision_Tree_IRIS.ipynb 467.1 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/internet_data.csv 459.4 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/EDA-Overview-Lending-Club.ipynb 354.9 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Car-price-prediction-linear-regression-Intro-version.ipynb 237.2 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/HR-Employee-Attrition.csv 226.5 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Random-Forest-Credit-Default-Prediction.ipynb 208.8 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/customer_data.csv 181.6 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Handwritten-Digit-MNIST-SVM.ipynb 112.9 kB
  • Chapter 13 Introduction to Deep Learning/001. Introduction to Deep Learning.en.srt 106.1 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/gapminderData.csv 82.1 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Car_sales.xls 72.2 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/PCA-Housing.ipynb 70.6 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Employee-Attrition-using-Naive-Bayes.ipynb 47.6 kB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/002. K-Means Clustering Computation.en.srt 41.8 kB
  • Chapter 04 Exploratory Data Analysis/009. EDA Project 7.en.srt 38.6 kB
  • Chapter 05 Linear Regression/002. Training and Cost Function.en.srt 35.6 kB
  • Chapter 06 Logistic Regression/002. Logit Model.en.srt 35.6 kB
  • Chapter 03 Learning Python/010. Python Sets 1.en.srt 34.4 kB
  • Chapter 06 Logistic Regression/006. Model Evaluation - AUC-ROC.en.srt 33.9 kB
  • Chapter 06 Logistic Regression/004. Data Analysis and Feature Engineering.en.srt 33.4 kB
  • Chapter 03 Learning Python/003. Python Variables and Conditions.en.srt 33.0 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/newhousing.csv 32.3 kB
  • Chapter 02 Statistical Techniques/008. Hypothesis Testing.en.srt 31.4 kB
  • Chapter 01 Introduction to Machine Learning/005. Machine Learning Use Cases and Types.en.srt 31.1 kB
  • Chapter 09 Random Forest Ensemble/002. Random Forest Steps Pruning and Optimization.en.srt 30.4 kB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/002. Principal Component Analysis - Computations 1.en.srt 30.0 kB
  • Chapter 01 Introduction to Machine Learning/008. Machine Learning Life Cycle and Pipelines.en.srt 29.4 kB
  • Chapter 02 Statistical Techniques/001. Statistics and Experiments.en.srt 28.9 kB
  • Chapter 03 Learning Python/023. Pandas DataFrame 5.en.srt 28.9 kB
  • Chapter 08 Decision Tree Classifier/002. Decision Tree - Learning Steps.en.srt 28.3 kB
  • Chapter 02 Statistical Techniques/002. Types of Data and Descriptive Statistics.en.srt 27.9 kB
  • Chapter 03 Learning Python/026. Python Lambda Functions.en.srt 27.7 kB
  • Chapter 05 Linear Regression/001. Linear Regression Introduction.en.srt 27.7 kB
  • Chapter 01 Introduction to Machine Learning/007. Challenges in Machine Learning.en.srt 27.5 kB
  • Chapter 10 Support Vector Machine/001. Support Vector Machine Concepts.en.srt 27.4 kB
  • Chapter 05 Linear Regression/006. OLS Assumptions and Testing.en.srt 26.7 kB
  • Chapter 02 Statistical Techniques/004. Histograms and Normal Approximation.en.srt 26.7 kB
  • Chapter 04 Exploratory Data Analysis/006. EDA Project 4.en.srt 26.7 kB
  • Chapter 05 Linear Regression/010. Data Preparation and Analysis 3.en.srt 26.5 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/CarPrice_Assignment.csv 26.5 kB
  • Chapter 09 Random Forest Ensemble/003. Model Building and Hyperparameter Tuning using Grid Search CV.en.srt 26.3 kB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/001. Principal Component Analysis - Concepts.en.srt 26.1 kB
  • Chapter 04 Exploratory Data Analysis/008. EDA Project 6.en.srt 25.8 kB
  • Chapter 02 Statistical Techniques/007. Binomial Theory - Expected Value and Standard Error.en.srt 25.7 kB
  • Chapter 09 Random Forest Ensemble/001. Ensemble Techniques Bagging and Random Forest.en.srt 25.7 kB
  • Chapter 06 Logistic Regression/003. Telecom Churn Case Study.en.srt 25.6 kB
  • Chapter 08 Decision Tree Classifier/005. Iris Dataset Case Study.en.srt 25.3 kB
  • Chapter 03 Learning Python/027. Python Lambda Functions and Date-Time Operations.en.srt 25.0 kB
  • Chapter 03 Learning Python/007. Python Tuples.en.srt 24.8 kB
  • Chapter 07 Naive Bayes Classification Algorithm/003. Employee Attrition Case Study.en.srt 24.3 kB
  • Chapter 04 Exploratory Data Analysis/002. Tools and Processes of EDA.en.srt 24.2 kB
  • Chapter 02 Statistical Techniques/005. Central Limit Theorem.en.srt 24.2 kB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/004. K-Means - Data Preparation and Modelling.en.srt 23.9 kB
  • Chapter 05 Linear Regression/004. Linear Regression - Practical Approach.en.srt 23.6 kB
  • Chapter 04 Exploratory Data Analysis/003. EDA Project 1.en.srt 23.5 kB
  • Chapter 04 Exploratory Data Analysis/001. Exploratory Data Analysis.en.srt 23.4 kB
  • Chapter 01 Introduction to Machine Learning/004. History of Machine Learning.en.srt 23.1 kB
  • Chapter 05 Linear Regression/011. Model Building.en.srt 23.0 kB
  • Chapter 03 Learning Python/017. Pandas Series 3.en.srt 23.0 kB
  • Chapter 03 Learning Python/016. Pandas Series 2.en.srt 22.9 kB
  • Chapter 08 Decision Tree Classifier/001. Decision Tree - Model Concept.en.srt 22.7 kB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/001. Unsupervised Learning - K-Mean Clustering.en.srt 22.7 kB
  • Chapter 08 Decision Tree Classifier/006. Model Optimization using Grid Search Cross Validation.en.srt 22.6 kB
  • Chapter 07 Naive Bayes Classification Algorithm/002. Naive Bayes Probability Computation.en.srt 22.5 kB
  • Chapter 08 Decision Tree Classifier/003. Gini Index and Entropy Measures.en.srt 22.4 kB
  • Chapter 01 Introduction to Machine Learning/001. Course Introduction.en.srt 22.3 kB
  • Chapter 04 Exploratory Data Analysis/007. EDA Project 5.en.srt 22.2 kB
  • Chapter 07 Naive Bayes Classification Algorithm/004. Model Building and Optimization.en.srt 22.0 kB
  • Chapter 07 Naive Bayes Classification Algorithm/001. Naive Bayes Probability Model.en.srt 21.6 kB
  • Chapter 06 Logistic Regression/007. Model Optimization 1.en.srt 21.5 kB
  • Chapter 05 Linear Regression/009. Data Preparation and Analysis 2.en.srt 21.5 kB
  • Chapter 04 Exploratory Data Analysis/004. EDA Project 2.en.srt 21.4 kB
  • Chapter 03 Learning Python/015. Pandas Series 1.en.srt 21.2 kB
  • Chapter 03 Learning Python/019. Pandas DataFrame 1.en.srt 21.1 kB
  • Chapter 06 Logistic Regression/001. Logistic Regression Introduction.en.srt 21.0 kB
  • Chapter 03 Learning Python/006. Python Lists.en.srt 20.9 kB
  • Chapter 05 Linear Regression/012. Model Evaluation and Optimization.en.srt 20.9 kB
  • Chapter 03 Learning Python/025. Python User Defined Functions.en.srt 20.9 kB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/004. Principal Component Analysis Practicals.en.srt 20.8 kB
  • Chapter 05 Linear Regression/007. Car Price Prediction.en.srt 20.7 kB
  • Chapter 03 Learning Python/018. Pandas Series 4.en.srt 20.7 kB
  • Chapter 03 Learning Python/024. Pandas DataFrame 6.en.srt 20.6 kB
  • Chapter 03 Learning Python/008. Python Dictionaries 1.en.srt 20.4 kB
  • Chapter 03 Learning Python/013. Numpy Arrays 2.en.srt 20.3 kB
  • Chapter 01 Introduction to Machine Learning/003. Machine Learning Terminology.en.srt 20.3 kB
  • Chapter 03 Learning Python/012. Numpy Arrays 1.en.srt 20.0 kB
  • Chapter 10 Support Vector Machine/002. Support Vector Machine Metrics and Polynomial SVM.en.srt 19.9 kB
  • Chapter 03 Learning Python/020. Pandas DataFrame 2.en.srt 19.7 kB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/005. K-Means - Model Optimization.en.srt 19.6 kB
  • Chapter 06 Logistic Regression/008. Model Optimization 2.en.srt 19.5 kB
  • Chapter 05 Linear Regression/008. Data Preparation and Analysis 1.en.srt 19.3 kB
  • Chapter 03 Learning Python/004. Python Iterations 1.en.srt 19.2 kB
  • Chapter 10 Support Vector Machine/003. Support Vector Machine Project 1.en.srt 19.1 kB
  • Chapter 05 Linear Regression/003. Cost Functions and Gradient Descent.en.srt 19.0 kB
  • Chapter 01 Introduction to Machine Learning/011. Classification Problems and Performance Metrics.en.srt 18.9 kB
  • Chapter 03 Learning Python/014. Numpy Arrays 3.en.srt 18.9 kB
  • Chapter 03 Learning Python/022. Pandas DataFrame 4.en.srt 18.9 kB
  • Chapter 03 Learning Python/021. Pandas DataFrame 3.en.srt 18.5 kB
  • Chapter 11 Dimensionality Reduction - Principal Component Analysis (PCA)/003. Principal Component Analysis - Computations 2.en.srt 18.2 kB
  • Chapter 03 Learning Python/028. Python String Operations.en.srt 18.1 kB
  • Chapter 06 Logistic Regression/005. Build the Logistic Model.en.srt 18.1 kB
  • Chapter 05 Linear Regression/005. Feature Scaling and Cost Functions.en.srt 17.9 kB
  • Chapter 02 Statistical Techniques/006. Probability Theory.en.srt 17.5 kB
  • Chapter 01 Introduction to Machine Learning/010. Regression Models and Performance Metrics.en.srt 17.5 kB
  • Chapter 01 Introduction to Machine Learning/002. Introduction to Machine Learning.en.srt 17.4 kB
  • Chapter 08 Decision Tree Classifier/004. Pruning and Hyperparameter Tuning.en.srt 15.4 kB
  • Chapter 01 Introduction to Machine Learning/009. Regression Problems.en.srt 15.2 kB
  • Chapter 03 Learning Python/002. Starting with Python with Jupyter Notebook.en.srt 15.0 kB
  • Chapter 03 Learning Python/005. Python Iterations 2.en.srt 14.7 kB
  • Chapter 01 Introduction to Machine Learning/012. Optimizing Classification Metrics.en.srt 13.8 kB
  • Chapter 04 Exploratory Data Analysis/005. EDA Project 3.en.srt 13.7 kB
  • Chapter 09 Random Forest Ensemble/004. Optimization Continued.en.srt 13.6 kB
  • Chapter 10 Support Vector Machine/005. Support Vector Machine - Classifying Polynomial Data.en.srt 13.5 kB
  • Chapter 01 Introduction to Machine Learning/013. Bias and Variance.en.srt 13.3 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Fashion_MNIST_Image_Classification_using_Deep_Learning_tf_Keras.ipynb 12.8 kB
  • Chapter 12 Unsupervised Learning using K-Means Clustering/003. K-Means Clustering Optimization.en.srt 12.6 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Car_sales.csv 11.9 kB
  • Chapter 03 Learning Python/001. Introduction to Python.en.srt 11.1 kB
  • Chapter 01 Introduction to Machine Learning/006. Role of Data in Machine Learning.en.srt 8.9 kB
  • Chapter 02 Statistical Techniques/003. Random Variables and Normal Distribution.en.srt 8.6 kB
  • Chapter 03 Learning Python/009. Python Dictionaries 2.en.srt 6.9 kB
  • Chapter 10 Support Vector Machine/004. Support Vector Machine Predictions.en.srt 6.0 kB
  • Chapter 05 Linear Regression/013. Model Optimization.en.srt 4.7 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/iris_csv.csv 4.6 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/Code-and-Data-Files/Mall_Customers.csv 3.8 kB
  • Chapter 03 Learning Python/011. Python Sets 2.en.srt 2.8 kB
  • z.Mastering-Machine-Learning-Algorithms-using-Python-main/README.md 52 Bytes

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