Description

The purpose of this course is to teach you how to use Python for machine learning to create real-world algorithms. You will gain an in-depth understanding of the fundamentals of deep learning. This course will help you explore different frameworks in Python to solve real-world problems using the core concepts of deep learning and artificial intelligence.

Machine learning is a branch of computer science in which you can use mathematical input to develop complicated models that fulfil various roles. Python is a popular choice for building machine learning models because of the large number of libraries available. This course will walk you through an astonishing combination of Python and machine learning, teaching you the fundamentals of machine learning so you can construct your own projects. You'll begin this course by studying Python programming and applying Scikit-Learn to machine learning regression. This lays the groundwork for understanding the theory underpinning simple and multiple linear regression algorithms. Following that, you'll learn how to solve linear and logistic regression issues. The courses further guides you to harness the power of sklearn, grasping the theory and practical application of logistic regression, and then advances to cover the math underpinning decision trees. Finally, you'll learn about the various clustering algorithms. By the end of this course, you will be able to use these machine learning algorithms in the real world.

What You Will Learn

Learn the fundamentals of the deep learning theory
Explore classification algorithms for K-Nearest Neighbors, decision tree, and logistic regression
Learn to implement ANN and CNN in Python
Understand the gradient descent algorithm
Explore the different types of activation functions
Explore neural network architecture

Audience

This course is for anyone interested in pursuing a career in machine learning, as well as Python programmers who want to add machine learning skills to their resume. This course will also benefit technologists who want to learn more about how machine learning works in the real world. This course requires familiarity with the fundamentals of Python, as well as readiness, flexibility, a will to learn, and, most importantly, basic mathematical skills.

Approach

This course is a balance of theory and practical demonstrations in which we will start with the fundamentals and work our way up to implementation. We will be utilizing Python 3.9.7 and Juypter Notebook 6.4.5 in this course.

Key Features

Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence (AI) * Build artificial neural networks with TensorFlow and Keras * Make predictions using linear regression, polynomial regression, and multivariate regression

Github Repo

https://github.com/PacktPublishing/Python-for-Machine-Learning---The-Complete-Beginner-s-Course

About the Author

Meta Brains

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!

Course Outline

1. Introduction to Machine Learning

In this section, we will have a quick introduction to machine learning.

1. What Is Machine Learning?

In this video, we will understand machine learning.

2. Applications of Machine Learning

In this video, we will cover applications of machine learning.

3. Machine Learning Methods

In this video, we will cover machine learning methods.

4. What Is Supervised Learning?

In this video, we will understand supervised learning.

5. What Is Unsupervised Learning?

In this video, we will understand unsupervised learning.

6. Supervised Learning Versus Unsupervised Learning

In this video, we will cover supervised learning versus unsupervised learning.

2. Optional: Setting Up Python and ML Algorithms Implementation

In this section, you will learn how to set up Python and ML algorithms implementation.

1. Introduction

In this video, we will get a quick section introduction about the process of setting up the Python environment and implementing ML algorithms that will be covered in the upcoming videos.

2. Python Libraries for Machine Learning

In this video, we will cover Python libraries for machine learning.

3. Setting Up Python

In this video, you will learn how to set up Python.

4. What Is Jupyter?

In this video, we will understand Jupyter Notebook.

5. Anaconda Installation Windows Mac and Ubuntu

In this video, we will work on Anaconda installation for Windows, Mac, and Ubuntu.

6. Implementing Python in Jupyter

In this video, you will learn how to implement Python in Jupyter.

7. Managing Directories in Jupyter Notebook

In this video, you will learn how to manage directories in Jupyter Notebook.

3. Simple Linear Regression

In this section, we will cover simple linear regression.

1. Introduction to Regression

In this video, we will have a quick introduction to regression.

2. How Does Linear Regression Work?

In this video, we will understand how linear regression works.

3. Line Representation

In this video, we will cover line representation.

4. Implementation in Python: Importing Libraries and Datasets

In this video, you will learn how to import libraries and datasets.

5. Implementation in Python: Distribution of the Data

In this video, you will learn distribution of the data.

6. Implementation in Python: Creating a Linear Regression Object

In this video, you will learn how to create a linear regression object.

4. Multiple Linear Regression

In this section, we will cover multiple linear regression.

1. Understanding Multiple Linear Regression

In this video, we will understand multiple linear regression.

2. Implementation in Python: Exploring the Dataset

In this video, we will first explore our dataset, then learn how to import and read our dataset in Python.

3. Implementation in Python: Encoding Categorical Data

In this video, you will learn how to encode categorical data.

4. Implementation in Python: Splitting Data into Train and Test Sets

In this video, you will learn how to split data into train and test sets.

5. Implementation in Python: Training the Model on the Training Set

In this video, you will learn how to train the model on the training set.

6. Implementation in Python: Predicting the Test Set Results

In this video, you will learn how to predict the test set results.

7. Evaluating the Performance of the Regression Model

In this video, you will learn how to evaluate the performance of the regression model.

8. Root Mean Squared Error in Python

In this video, we will cover root mean squared error in Python.

5. Classification Algorithms: K-Nearest Neighbors

In this section, we will cover classification algorithms: K-Nearest Neighbors.

1. Introduction to Classification

In this video, we will have a quick introduction to classification.

2. K-Nearest Neighbors Algorithm

In this video, we will cover the K-Nearest Neighbors algorithm.

3. Example of KNN

In this video, we will cover an example of KNN.

4. K-Nearest Neighbors (KNN) Using Python

In this video, we will cover K-Nearest Neighbors (KNN) using Python.

5. Implementation in Python: Importing Required Libraries

In this video, you will learn how to import the required libraries.

6. Implementation in Python: Importing the Dataset

In this video, you will learn how to import the dataset.

7. Implementation in Python: Splitting Data into Train and Test Sets

In this video, you will learn how to split data into train and test sets.

8. Implementation in Python: Feature Scaling

In this video, you will learn about feature scaling.

9. Implementation in Python: Importing the KNN Classifier

In this video, you will learn how to import the KNN classifier.

10. Implementation in Python: Results Prediction and Confusion Matrix

In this video, you will learn about results prediction and confusion matrix.

6. Classification Algorithms: Decision Tree

In this section, we will cover classification algorithms: decision tree.

1. Introduction to Decision Trees

In this video, we will have a quick introduction to decision trees.

2. What Is Entropy?

In this video, we will understand entropy.

3. Exploring the Dataset

In this video, you will learn how to explore the dataset.

4. Decision Tree Structure

In this video, we will cover the decision tree structure.

5. Implementation in Python: Importing Libraries and Datasets

In this video, you will learn how to import libraries and datasets.

6. Implementation in Python: Encoding Categorical Data

In this video, you will learn how to encode categorical data.

7. Implementation in Python: Splitting Data into Train and Test Sets

In this video, you will learn how to split data into train and test sets.

8. Implementation in Python: Results Prediction and Accuracy

In this video, you will learn about results prediction and accuracy.

7. Classification Algorithms: Logistic Regression

In this section, we will cover classification algorithms: logistic regression.

1. Introduction

In this video, we will have a quick introduction.

2. Implementation Steps

In this video, you will learn the implementation steps.

3. Implementation in Python: Importing Libraries and Datasets

In this video, you will learn how to import libraries and datasets.

4. Implementation in Python: Splitting Data into Train and Test Sets

In this video, you will learn how to split data into train and test sets.

5. Implementation in Python: Pre-Processing

In this video, you will learn how to do pre-processing.

6. Implementation in Python: Training the Model

In this video, you will learn how to train the model.

7. Implementation in Python: Results Prediction and Confusion Matrix

In this video, you will learn about results prediction and confusion matrix.

8. Logistic Regression Versus Linear Regression

In this video, we will cover logistic regression versus linear regression.

8. Clustering

In this section, you will learn about clustering.

1. Introduction to Clustering

In this video, we will have a quick introduction to clustering.

2. Use Cases

In this video, we will cover use cases.

3. K-Means Clustering Algorithm

In this video, we will cover the K-Means clustering algorithm.

4. Elbow Method

In this video, we will cover the elbow method.

5. Steps of the Elbow Method

In this video, we will cover the steps of the elbow method.

6. Implementation in Python

In this video, you will learn implementation in Python.

7. Hierarchical Clustering

In this video, we will cover hierarchical clustering.

8. Density-Based Clustering

In this video, we will cover density-based clustering.

9. Implementation of K-Means Clustering in Python

In this video, you will learn how to implement K-Means clustering in Python.

10. Importing the Dataset

In this video, you will learn how to import the dataset.

11. Visualizing the Dataset

In this video, you will learn how to visualize the dataset.

12. Defining the Classifier

In this video, you will learn how to define the classifier.

13. 3D Visualization of the Clusters

In this video, we will cover 3D visualization of the clusters.

14. 3D Visualization of the Predicted Values

In this video, we will cover 3D visualization of the predicted values.

15. Number of Predicted Clusters

In this video, you will learn about the number of predicted clusters.

9. Recommender System

In this section, we will cover the recommender system.

1. Introduction

In this video, we will have a quick introduction.

2. Collaborative Filtering in Recommender Systems

In this video, you will learn about collaborative filtering in recommender systems.

3. Content-Based Recommender System

In this video, you will learn about the content-based recommender system.

4. Implementation in Python: Importing Libraries and Datasets

In this video, you will learn how to import libraries and datasets.

5. Merging Datasets into One Dataframe

In this video, you will learn how to merge datasets into one dataframe.

6. Sorting by Title and Rating

In this video, you will learn how to sort by title and rating.

7. Histogram Showing Number of Ratings

In this video, you will learn how a histogram shows the number of ratings.

8. Frequency Distribution

In this video, you will learn about frequency distribution.

9. Jointplot of the Ratings and Number of Ratings

In this video, you will learn how to make a Jointplot of the ratings and number of ratings.

10. Data Pre-Processing

In this video, you will learn about data pre-processing.

11. Sorting the Most-Rated Movies

In this video, you will learn how to sort the most-rated movies.

12. Grabbing the Ratings for Two Movies

In this video, you will learn how to grab the ratings for two movies.

13. Correlation Between the Most-Rated Movies

In this video, you will learn about correlation between the most-rated movies.

14. Sorting the Data by Correlation

In this video, you will learn how to sort the data by correlation.

15. Filtering Out Movies

In this video, you will learn how to filter out movies.

16. Sorting Values

In this video, you will learn about sorting values.

17. Repeating the Process for Another Movie

In this video, you will learn how to repeat the process for another movie.

10. Conclusion

In this section, we will summarize our learning.

1. Thank You

Thank you! Happy learning!

Course Images

Python for Machine Learning - The Complete Beginner's Course

By Packt

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Highlights

Description

What You Will Learn

Audience

Approach

Key Features

Github Repo

About the Author

Meta Brains

Course Outline

1. Introduction to Machine Learning

2. Optional: Setting Up Python and ML Algorithms Implementation

3. Simple Linear Regression

4. Multiple Linear Regression

5. Classification Algorithms: K-Nearest Neighbors

6. Classification Algorithms: Decision Tree

7. Classification Algorithms: Logistic Regression

8. Clustering

9. Recommender System

10. Conclusion

Course Content

About The Provider

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