Python and R for Machine Learning & Deep Learning

Name: Python and R for Machine Learning & Deep Learning
Rating: 4.6 (11 reviews)

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

Created byManuel Ernesto Cambota

Last updated 10/2024

English

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

Course content

15 sections • 252 lectures • 32h 9m total length

Overview3:18

Introduction to Machine Learning16:03
Building a Machine Learning Model8:42
Formulate the business problem as a statistical task, tidy and preprocess data, split into train and test sets, train and validate the model, and deploy a monitoring pipeline for predictions.
Gathering Business Knowledge3:19
Data Exploration3:19
Dataset & Data Dictionary7:28
Importing Data in Python6:03
Launch a Jupyter notebook via Anaconda, set working directory, and import the house price data with pandas read_csv, then inspect df.head and df.shape to confirm 506 observations and 19 variables.
Importing the dataset into R3:00
Univariate analysis and EDD3:33
EDD in Python12:11
EDD in R12:43
Outlier Treatment4:15
Identify and treat outliers in data using box plots, scatter plots, and histograms; impute values with methods like capping at 99th percentile, extrapolation, or sigma-based replacement to improve prediction accuracy.
Outlier Treatment in Python14:18
Outlier Treatment in R4:49
Missing Value Imputation3:36
Missing Value Imputation in Python4:57
Identify missing values with df.info, then impute the n host beds column using fillna with its mean, updating the dataframe; apply column-wise fillna with mean for all columns if needed.
Missing Value imputation in R3:49
Seasonality in Data3:34
Bi-variate analysis and Variable transformation16:14
Variable transformation and deletion in Python9:21
Variable transformation in R9:37
Non-usable variables4:44
Dummy variable creation: Handling qualitative data4:50
Dummy variable creation in Python5:45
Dummy variable creation in R5:01
Correlation Analysis10:05
Correlation Analysis in Python7:07
Correlation Matrix in R8:09
The Problem Statement1:25
Basic Equations and Ordinary Least Squares (OLS) method8:13
Assessing accuracy of predicted coefficients14:40
Assessing Model Accuracy: RSE and R squared7:19
Simple Linear Regression in Python14:06
Simple Linear Regression in R7:40
Multiple Linear Regression4:57
The F - statistic8:22
Interpreting results of Categorical variables5:04
Multiple Linear Regression in Python14:13
Multiple Linear Regression in R7:50
Test-train split9:32
Bias Variance trade-off6:01
Test train split in Python10:19
Test-Train Split in R8:44
Regression models other than OLS4:18
Subset selection techniques11:34
SubShrinkage methods: Ridge and Lassoset selection in R7:14
Ridge regression and Lasso in Python23:50
Heteroscedasticity2:30
Ridge Regression and Lasso in R12:51
importing the data into Python4:56
Launch Jupyter Notebook, set the working directory, import numpy, pandas, and seaborn, and load the house price data from a csv using pandas read_csv, handling Windows paths.
Importing the data into R3:00
Import the house pricing data from a CSV into R workspace using read.csv, assign it to df, and inspect with view df and str(df) showing 506 observations and 19 variables.
Three Classifiers and the Problem statement3:17
Why can't we use Linear Regression?4:32
Logistic Regression7:54
Learn how logistic regression models probabilities using the sigmoid function to classify credit defaults. Discover maximum likelihood estimation and how it handles outliers and boundary decisions for predicting defaults.
Training a Simple Logistic Model in Python12:25
Training a Simple Logistic model in R1:48
Result of Simple Logistic Regression5:11
Shows a simple logistic regression with one predictor, estimating beta zero and beta one to compute p(y=1) and using p value to confirm price's non-zero effect.
Logistic with multiple predictors2:22
Training multiple predictor Logistic model in Python6:05
Training multiple predictor Logistic model in R1:48
Run logistic regression in R with multiple predictors using the dot notation to include all variables except the dependent, here sold, and interpret coefficients and p-values to identify significant predictors.
Confusion Matrix3:47
Learn how a confusion matrix compares model predictions to true values, distinguishing false positive (type one error) and false negative (type two error), and adjust thresholds for error costs.
Creating Confusion Matrix in Python9:55
Evaluating performance of model7:40
Evaluating model performance in Python2:21
Predicting probabilities, assigning classes and making Confusion Matrix in R6:23
Linear Discriminant Analysis9:42
LDA in Python2:30
Linear Discriminant Analysis in R9:10
Test-Train Split9:30
Test-Train Split in Python6:46
Test-Train Split in R9:27
K-Nearest Neighbors classifier8:41
K-Nearest Neighbors in Python: Part 15:51
K-Nearest Neighbors in Python: Part 27:00
K-Nearest Neighbors in R8:50
Understanding the results of classification models6:06
Summary of the three models4:32
Basics of Decision Trees10:10
Understanding a Regression Tree10:17
Stopping criteria for controlling tree growth3:15
Importing the Data set into Python5:40
Importing the Data set into R6:26
Missing value treatment in Python3:38
Dummy Variable creation in Python4:58
Dependent- Independent Data split in Python4:02
Test-Train split in Python6:04
Splitting Data into Test and Train Set in R5:30
Split data into train and test in R using an 80/20 ratio with the CA tools package, and set a seed for reproducibility.
Creating Decision tree in Python3:47
Building a Regression Tree in R14:18
Evaluating model performance in Python4:10
Plotting decision tree in Python4:58
Pruning a tree4:16
Pruning a tree in Python10:37
Pruning a Tree in R9:18
Ensemble technique 1 - Bagging6:39
Ensemble technique 1 - Bagging in Python11:05
Bagging in R6:20
Ensemble technique 2 - Random Forests3:56
Ensemble technique 2 - Random Forests in Python6:06
Using Grid Search in Python12:14
Random Forest in R3:58
Boosting7:10
Ensemble technique 3a - Boosting in Python5:08
Gradient Boosting in R7:10
Ensemble technique 3b - AdaBoost in Python4:00
AdaBoosting in R9:44
Ensemble technique 3c - XGBoost in Python11:07
XGBoosting in R16:08
Content Flow1:34
Concept of a Hyperplane4:55
Maximum Margin Classifier3:18
Limitations of Maximum Margin Classifier2:28
Support Vector classifiers10:00
Limitations of Support Vector Classifiers1:34
Kernel Based Support Vector Machines6:45
Regression and Classification Models0:46
Importing and preprocessing data in Python5:40
Standardizing the data6:28
SVM based Regression Model in Pytho10:08
Classification model - Preprocessing8:25
Classification model - Standardizing the data1:57
SVM Based classification model11:28
Hyper Parameter Tuning9:47
Polynomial Kernel with Hyperparameter Tuning4:07
Radial Kernel with Hyperparameter Tuning6:31
Importing and preprocessing data in R8:00
Classification SVM model using Linear Kernel16:11
Hyperparameter Tuning for Linear Kernel6:28
Polynomial Kernel with Hyperparameter Tuning10:19
Radial Kernel with Hyperparameter Tuning6:31
SVM based Regression Model in R11:14

Keras and Tensorflow3:04
Installing Tensorflow and Keras4:04
Dataset for classification7:19
Normalization and Test-Train split5:59
Different ways to create ANN using Keras1:58
Building the Neural Network using Keras12:24
Compiling and Training the Neural Network model10:34
Evaluating performance and Predicting using Keras9:21
Building Neural Network for Regression Problem22:10
Using Functional API for complex architectures12:40
Saving - Restoring Models and Using Callbacks19:49
Hyperparameter Tuning9:05

Installing Keras and Tensorflow2:54
Data Normalization and Test-Train Split12:00
Building,Compiling and Training14:57
Evaluating and Predicting9:46
ANN with NeuralNets Package8:07
Learn to build a neural network in R without Keras, using the neural nets package with two hidden layers (3 and 2 neurons), and predict pass/fail from test data.
Building Regression Model with Functional API12:34
Complex Architectures using Functional API8:50
Saving - Restoring Models and Using Callbacks20:16

CNN Introduction7:42
Stride2:51
Padding5:07
Filters and Feature maps7:48
Channels6:31
PoolingLayer5:32
CNN model in Python - Preprocessing5:42
CNN model in Python - structure and Compile6:24
CNN model in Python - Training and results6:50
Comparison - Pooling vs Without Pooling in Python6:20
CNN on MNIST Fashion Dataset - Model Architecture2:04
Data Preprocessin7:08
Creating Model Architecture6:04
Compiling and training2:53
Model Performance6:26
Comparison - Pooling vs Without Pooling in R4:33

Project - Introduction7:04
Data for the project0:01
Project - Data Preprocessing in Python9:19
Project - Training CNN model in Python9:05
Project in Python - model results3:07
Project in R - Data Preprocessing10:28
CNN Project in R - Structure and Compile4:59
Project in R - Training2:57
Project in R - Model Performance2:22
Project in R - Data Augmentation7:12
Augment training data with an image data generator applying rotation, shift, shear, zoom, and horizontal flip, using fill mode to handle new pixels.
Project in R - Validation Performanc2:24
Project - Data Augmentation Preprocessing6:46
Project - Data Augmentation Training and Results6:26

Requirements

No Pre-requisites

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 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.

Python and R for Machine Learning & Deep Learning

What you'll learn

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Course content

Introduction1 lecture • 3min

Python & Jupyter Notebook - Essentials8 lectures • 1hr 29min

R Studio & R Crash8 lectures • 1hr 2min

Statistics - Basics5 lectures • 30min

Machine Learning130 lectures • 15hr 35min

Deep Learning9 lectures • 1hr 42min

ANN in Python12 lectures • 1hr 58min

ANN in R8 lectures • 1hr 29min

CNN - Basics16 lectures • 1hr 30min

Project - Creating CNN Model in Python13 lectures • 1hr 12min

Requirements

Description

Who this course is for: