Python and R for Machine Learning & Deep Learning

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Manuel Ernesto Cambota

32:09:16

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1. Introduction

1. Overview.mp4

03:18

2. Python & Jupyter Notebook - Essentials

1. Installing Python & Anaconda.mp4

03:04

2. Jupyter Overview.mp4

13:26

3. Python Basics.mp4

04:28

4. Python Basics 2.mp4

19:07

5. Python Basics 3.mp4

18:41

6. Numpy.mp4

11:54

7. Pandas.mp4

09:15

8. Seaborn.mp4

08:57

3. R Studio & R Crash

1. Installing R & Studio.mp4

05:52

2. R & R Studio - Basics.mp4

10:47

3. Packages in R.mp4

10:52

4. Inbuilt datasets of R.mp4

04:21

5. Manual data entry.mp4

03:11

6. Importing from CSV or Text files.mp4

06:49

7. Barplots.mp4

13:42

8. Histograms.mp4

06:01

4. Statistics - Basics

1. Types of Data.mp4

04:04

2. Types of Statistics.mp4

02:45

3. Describing data Graphically.mp4

11:37

4. Measures of Centers.mp4

07:05

5. Measures of Dispersion.mp4

04:37

5. Machine Learning

1. Introduction to Machine Learning.mp4

16:03

2. Building a Machine Learning Model.mp4

08:42

3. Gathering Business Knowledge.mp4

03:19

4. Data Exploration.mp4

03:19

5. Dataset & Data Dictionary.mp4

07:28

6. Importing Data in Python.mp4

06:03

7. Importing the dataset into R.mp4

03:00

8. Univariate analysis and EDD.mp4

03:33

9. EDD in Python.mp4

12:11

10. EDD in R.mp4

12:43

11. Outlier Treatment.mp4

04:15

12. Outlier Treatment in Python.mp4

14:18

13. Outlier Treatment in R.mp4

04:49

14. Missing Value Imputation.mp4

03:36

15. Missing Value Imputation in Python.mp4

04:57

16. Missing Value imputation in R.mp4

03:49

17. Seasonality in Data.mp4

03:34

18. Bi-variate analysis and Variable transformation.mp4

16:14

19. Variable transformation and deletion in Python.mp4

09:21

20. Variable transformation in R.mp4

09:37

21. Non-usable variables.mp4

04:44

22. Dummy variable creation Handling qualitative data.mp4

04:50

23. Dummy variable creation in Python.mp4

05:45

24. Dummy variable creation in R.mp4

05:01

25. Correlation Analysis.mp4

10:05

26. Correlation Analysis in Python.mp4

07:07

27. Correlation Matrix in R.mp4

08:09

28. The Problem Statement.mp4

01:25

29. Basic Equations and Ordinary Least Squares (OLS) method.mp4

08:13

30. Assessing accuracy of predicted coefficients.mp4

14:40

31. Assessing Model Accuracy RSE and R squared.mp4

07:19

32. Simple Linear Regression in Python.mp4

14:06

33. Simple Linear Regression in R.mp4

07:40

34. Multiple Linear Regression.mp4

04:57

35. The F - statistic.mp4

08:22

36. Interpreting results of Categorical variables.mp4

05:04

37. Multiple Linear Regression in Python.mp4

14:13

38. Multiple Linear Regression in R.mp4

07:50

39. Test-train split.mp4

09:32

40. Bias Variance trade-off.mp4

06:01

41. Test train split in Python.mp4

10:19

42. Test-Train Split in R.mp4

08:44

43. Regression models other than OLS.mp4

04:18

44. Subset selection techniques.mp4

11:34

45. SubShrinkage methods Ridge and Lassoset selection in R.mp4

07:14

46. Ridge regression and Lasso in Python.mp4

23:50

47. Heteroscedasticity.mp4

02:30

48. Ridge Regression and Lasso in R.mp4

12:51

49. importing the data into Python.mp4

04:56

50. Importing the data into R.mp4

03:00

51. Three Classifiers and the Problem statement.mp4

03:17

52. Why cant we use Linear Regression.mp4

04:32

53. Logistic Regression.mp4

07:54

54. Training a Simple Logistic Model in Python.mp4

12:25

55. Training a Simple Logistic model in R.mp4

01:48

56. Result of Simple Logistic Regression.mp4

05:11

57. Logistic with multiple predictors.mp4

02:22

58. Training multiple predictor Logistic model in Python.mp4

06:05

59. Training multiple predictor Logistic model in R.mp4

01:48

60. Confusion Matrix.mp4

03:47

61. Creating Confusion Matrix in Python.mp4

09:55

62. Evaluating performance of model.mp4

07:40

63. Evaluating model performance in Python.mp4

02:21

64. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4

06:23

65. Linear Discriminant Analysis.mp4

09:42

66. LDA in Python.mp4

02:30

67. Linear Discriminant Analysis in R.mp4

09:10

68. Test-Train Split.mp4

09:30

69. Test-Train Split in Python.mp4

06:46

70. Test-Train Split in R.mp4

09:27

71. K-Nearest Neighbors classifier.mp4

08:41

72. K-Nearest Neighbors in Python Part 1.mp4

05:51

73. K-Nearest Neighbors in Python Part 2.mp4

07:00

74. K-Nearest Neighbors in R.mp4

08:50

75. Understanding the results of classification models.mp4

06:06

76. Summary of the three models.mp4

04:32

77. Basics of Decision Trees.mp4

10:10

78. Understanding a Regression Tree.mp4

10:17

79. Stopping criteria for controlling tree growth.mp4

03:15

80. Importing the Data set into Python.mp4

05:40

81. Importing the Data set into R.mp4

06:26

82. Missing value treatment in Python.mp4

03:38

83. Dummy Variable creation in Python.mp4

04:58

84. Dependent- Independent Data split in Python.mp4

04:02

85. Test-Train split in Python.mp4

06:04

86. Splitting Data into Test and Train Set in R.mp4

05:30

87. Creating Decision tree in Python.mp4

03:47

88. Building a Regression Tree in R.mp4

14:18

89. Evaluating model performance in Python.mp4

04:10

90. Plotting decision tree in Python.mp4

04:58

91. Pruning a tree.mp4

04:16

92. Pruning a tree in Python.mp4

10:37

93. Pruning a Tree in R.mp4

09:18

94. Ensemble technique 1 - Bagging.mp4

06:39

95. Ensemble technique 1 - Bagging in Python.mp4

11:05

96. Bagging in R.mp4

06:20

97. Ensemble technique 2 - Random Forests.mp4

03:56

98. Ensemble technique 2 - Random Forests in Python.mp4

06:06

99. Using Grid Search in Python.mp4

12:14

100. Random Forest in R.mp4

03:58

101. Boosting.mp4

07:10

102. Ensemble technique 3a - Boosting in Python.mp4

05:08

103. Gradient Boosting in R.mp4

07:10

104. Ensemble technique 3b - AdaBoost in Python.mp4

04:00

105. AdaBoosting in R.mp4

09:44

106. Ensemble technique 3c - XGBoost in Python.mp4

11:07

107. XGBoosting in R.mp4

16:08

108. Content Flow.mp4

01:34

109. Concept of a Hyperplane.mp4

04:55

110. Maximum Margin Classifier.mp4

03:18

111. Limitations of Maximum Margin Classifier.mp4

02:28

112. Support Vector classifiers.mp4

10:00

113. Limitations of Support Vector Classifiers.mp4

01:34

114. Kernel Based Support Vector Machines.mp4

06:45

115. Regression and Classification Models.mp4

00:46

116. Importing and preprocessing data in Python.mp4

05:40

117. Standardizing the data.mp4

06:28

118. SVM based Regression Model in Pytho.mp4

10:08

119. Classification model - Preprocessing.mp4

08:25

120. Classification model - Standardizing the data.mp4

01:57

121. SVM Based classification model.mp4

11:28

122. Hyper Parameter Tuning.mp4

09:47

123. Polynomial Kernel with Hyperparameter Tuning.mp4

04:07

124. Radial Kernel with Hyperparameter Tuning.mp4

06:31

125. Importing and preprocessing data in R.mp4

08:00

126. Classification SVM model using Linear Kernel.mp4

16:11

127. Hyperparameter Tuning for Linear Kernel.mp4

06:28

128. Polynomial Kernel with Hyperparameter Tuning.mp4

10:19

129. Radial Kernel with Hyperparameter Tuning.mp4

06:31

130. SVM based Regression Model in R.mp4

11:14

6. Deep Learning

1. Introduction to Neural Networks and Course flow.mp4

04:38

2. Perceptron.mp4

09:47

3. Activation Functions.mp4

07:30

4. Python - Creating Perceptron model.mp4

14:10

5. Basic Terminologies.mp4

09:47

6. Gradient Descent.mp4

12:17

7. Back Propagation.mp4

22:27

8. Some Important Concepts.mp4

12:44

9. Hyperparameter.mp4

08:19

7. ANN in Python

1. Keras and Tensorflow.mp4

03:04

2. Installing Tensorflow and Keras.mp4

04:04

3. Dataset for classification.mp4

07:19

4. Normalization and Test-Train split.mp4

05:59

5. Different ways to create ANN using Keras.mp4

01:58

6. Building the Neural Network using Keras.mp4

12:24

7. Compiling and Training the Neural Network model.mp4

10:34

8. Evaluating performance and Predicting using Keras.mp4

09:21

9. Building Neural Network for Regression Problem.mp4

22:10

10. Using Functional API for complex architectures.mp4

12:40

11. Saving - Restoring Models and Using Callbacks.mp4

19:49

12. Hyperparameter Tuning.mp4

09:05

8. ANN in R

1. Installing Keras and Tensorflow.mp4

02:54

2. Data Normalization and Test-Train Split.mp4

12:00

3. Building,Compiling and Training.mp4

14:57

4. Evaluating and Predicting.mp4

09:46

5. ANN with NeuralNets Package.mp4

08:07

6. Building Regression Model with Functional API.mp4

12:34

7. Complex Architectures using Functional API.mp4

08:50

8. Saving - Restoring Models and Using Callbacks.mp4

20:16

9. CNN - Basics

1. CNN Introduction.mp4

07:42

2. Stride.mp4

02:51

3. Padding.mp4

05:07

4. Filters and Feature maps.mp4

07:48

5. Channels.mp4

06:31

6. PoolingLayer.mp4

05:32

7. CNN model in Python - Preprocessing.mp4

05:42

8. CNN model in Python - structure and Compile.mp4

06:24

9. CNN model in Python - Training and results.mp4

06:50

10. Comparison - Pooling vs Without Pooling in Python.mp4

06:20

11. CNN on MNIST Fashion Dataset - Model Architecture.mp4

02:04

12. Data Preprocessin.mp4

07:08

13. Creating Model Architecture.mp4

06:04

14. Compiling and training.mp4

02:53

15. Model Performance.mp4

06:26

16. Comparison - Pooling vs Without Pooling in R.mp4

04:33

10. Project - Creating CNN Model in Python

1. Project - Introduction.mp4

07:04

2. Data for the project.html

3. Project - Data Preprocessing in Python.mp4

09:19

4. Project - Training CNN model in Python.mp4

09:05

5. Project in Python - model results.mp4

03:07

6. Project in R - Data Preprocessing.mp4

10:28

7. CNN Project in R - Structure and Compile.mp4

04:59

8. Project in R - Training.mp4

02:57

9. Project in R - Model Performance.mp4

02:22

10. Project in R - Data Augmentation.mp4

07:12

11. Project in R - Validation Performanc.mp4

02:24

12. Project - Data Augmentation Preprocessing.mp4

06:46

13. Project - Data Augmentation Training and Results.mp4

06:26

11. Transfer Learning - Basics

1. ILSVRC.mp4

04:10

2. LeNET.mp4

01:31

3. VGG16NET.mp4

02:00

4. GoogLeNet.mp4

02:52

5. Transfer Learning.mp4

05:15

6. Project - Transfer Learning - VGG16.mp4

19:40

7. Project - Transfer Learning - VGG16 (Implementation).mp4

12:44

8. Project - Transfer Learning - VGG16 (Performance).mp4

08:02

12. Time Series Analysis & Forecasting

1. Introduction.mp4

02:10

2. Time Series Forecasting - Use cases.mp4

02:25

3. Forecasting model creation - Steps.mp4

02:46

4. Forecasting model creation - Steps 1 (Goal).mp4

06:03

5. Time Series - Basic Notations.mp4

09:02

13. Time Series - Processing in Python

1. Data Loading in Python.mp4

17:51

2. Time Series - Visualization Basics.mp4

09:28

3. Time Series - Visualization in Python.mp4

27:10

4. Time Series - Feature Engineering Basics.mp4

11:03

5. Time Series - Feature Engineering in Python.mp4

18:01

6. Time Series - Upsampling and Downsampling.mp4

04:17

7. Time Series - Upsampling and Downsampling in Python.mp4

16:45

8. Time Series - Power Transformation.mp4

02:32

9. Moving Average.mp4

07:12

10. Exponential Smoothing.mp4

02:07

11. White Noise.mp4

02:29

12. Random Walk.mp4

04:23

13. Decomposing Time Series in Python.mp4

09:41

14. Differencing.mp4

06:16

15. Differencing in Python.mp4

15:07

16. Test Train Split in Python.mp4

11:28

17. Naive (Persistence) model in Python.mp4

07:54

18. Auto Regression Model - Basics.mp4

03:29

19. Auto Regression Model creation in Python.mp4

09:22

20. Auto Regression with Walk Forward validation in Python.mp4

08:20

21. Moving Average model -Basics.mp4

04:33

22. Moving Average model in Python.mp4

08:58

14. Time Series - ARIMA Model

1. ACF and PACF.mp4

08:07

2. ARIMA model - Basics.mp4

04:43

3. ARIMA model in Python.mp4

13:15

4. ARIMA model with Walk Forward Validation in Python.mp4

05:24

15. Time Series - SARIMA Model

1. SARIMA model.mp4

07:26

2. SARIMA model in Python.mp4

10:40

3. Stationary time Series.mp4

01:42

Description

Learn Machine Learning and Deep Learning using Python and R in 2024

What You'll Learn?

Basics to advanced Python programming
Data manipulation with Pandas
Visualization with Matplotlib and Seaborn
Fundamentals of R
Statistical modeling in R
Introduction to neural networks
Building models with TensorFlow and Keras
Convolutional and Recurrent Neural Networks
Comprehensive understanding of machine learning and deep learning

Who is this for?

IT Professionals: Broaden your career prospects by transitioning into the field of data science

Students: Whether you’re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts

Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning.

Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge.

Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise.

What You Need to Know?

No Pre-requisites

More details

Description
Welcome to the gateway to your journey into Python for Machine Learning & Deep Learning!
Unlock the power of Python and delve into the realms of Machine Learning and Deep Learning with our comprehensive course. Whether you're a beginner eager to step into the world of artificial intelligence or a seasoned professional looking to enhance your skills, this course is designed to cater to all levels of expertise.
What sets this course apart?
Comprehensive Curriculum: Our meticulously crafted curriculum covers all the essential concepts of Python programming, machine learning algorithms, and deep learning architectures. From the basics to advanced techniques, we've got you covered.
Hands-On Projects: Theory is important, but practical experience is paramount. Dive into real-world projects that challenge you to apply what you've learned and reinforce your understanding.
Expert Guidance: Learn from industry expert who has years of experience in the field. Benefit from his insights, tips, and best practices to accelerate your learning journey.
Interactive Learning: Engage in interactive lessons, quizzes, and exercises designed to keep you motivated and actively involved throughout the course.
Flexibility: Life is busy, and we understand that. Our course offers flexible scheduling options, allowing you to learn at your own pace and convenience.
Career Opportunities: Machine Learning and Deep Learning are in high demand across various industries. By mastering these skills, you'll open doors to exciting career opportunities and potentially higher earning potential.
Are you ready to embark on an exhilarating journey into the world of Python for Machine Learning & Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!
Who this course is for:
IT Professionals: Broaden your career prospects by transitioning into the field of data science
Students: Whether youâ€™re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts
Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning.
Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge.
Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise.

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Python

Machine Learning

Deep Learning

R Programming

Manuel Ernesto Cambota

Instructor's Courses

------------------PORTUGUESE-------------------------Formado em Engenharia de Computação pela Universidade Técnica de Angola, actua nas áreas de Análise e desenvolvimento de sistemas com cerca de 10 anos de experiência.Trabalha com SAP, Bancos de Dados, Java, JSF, Hibernate, Primefaces, Bootstrap, PHP e .NET. É apaixonado pelas novas tecnologias de informação, pelo Ensino a distância, pelo Sporting de Portugal, pelo Barcelona FC e pela Selecção brasileira de Futebol(mesmo não sendo brasileiro). ------------------ENGLISH-------------------------I'm graduated in Computer Engineering at Technical University of Angola. I work in the areas of Analysis and systems development, haveing about 10 years of experience. I use SAP, Databases, Java, JSF, Hibernate, Primefaces, Bootstrap, PHP and .NET. I am passionate about Information Technologies, e-learning, Sporting Portugal FC, Barcelona FC and the Brazilian national football team (even though I'm not Brazilian).

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