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Python for Machine Learning: The Complete Beginner's Course

Apex Learning

85 Lessons (2h)

Lifetime

Experience level required: All levels
Access 85 lectures & 2 hours of content 24/7
Length of time users can access this course: Lifetime

Course Curriculum

85 Lessons (2h)

Your First Program
Preview

Section 01: Introduction to Machine Learning

What is Machine Learning?1:53
Applications of Machine Learning1:48
Machine learning Methods0:29
What is Supervised learning?1:18
What is Unsupervised learning?0:59
Supervised learning vs Unsupervised learning3:36
Section 02: Setting Up Python & ML Algorithms Implementation

Python Libraries for Machine Learning1:44
Introduction S20:53
Setting up Python2:25
What is Jupyter?1:33
Anaconda Installation Windows Mac and Ubuntu4:16
Implementing Python in Jupyter0:45
Managing Directories in Jupyter Notebook2:48
Section 03: Simple Linear Regression

How Does Linear Regression Work?1:36
Introduction to regression1:41
Line representation0:58
Implementation in Python: Importing libraries & datasets1:48
Implementation in Python: Distribution of the data2:19
Implementation in Python: Creating a linear regression object2:56
Section 04: Multiple Linear Regression

Implementation in Python: Exploring the dataset3:53
Understanding Multiple linear regression1:34
Implementation in Python: Encoding Categorical Data4:47
Implementation in Python: Splitting data into Train and Test Sets1:48
Implementation in Python: Training the model on the Training set1:23
Implementation in Python: Predicting the Test Set results2:59
Evaluating the performance of the regression model1:20
Root Mean Squared Error in Python2:30
Section 05: Classification Algorithms: K-Nearest Neighbors

Introduction to classification1:05
K-Nearest Neighbors algorithm0:55
Example of KNN0:30
K-Nearest Neighbours (KNN) using python1:15
Implementation in Python: Importing required libraries0:51
Implementation in Python: Importing the dataset1:35
Implementation in Python: Splitting data into Train and Test Sets3:16
Implementation in Python: Feature Scaling0:26
Implementation in Python: Importing the KNN classifier2:05
Implementation in Python: Results prediction & Confusion matrix1:32
Section 06: Classification Algorithms: Decision Tree

What is Entropy?1:17
Introduction to decision trees1:23
Exploring the dataset0:36
Decision tree structure1:16
Implementation in Python: Importing libraries & datasets0:48
Implementation in Python: Encoding Categorical Data2:50
Implementation in Python: Splitting data into Train and Test Sets1:06
Implementation in Python: Results Prediction & Accuracy2:37
Section 07: Classification Algorithms: Logistic regression

Implementation steps0:52
Introduction S71:25
Implementation in Python: Importing libraries & datasets2:01
Implementation in Python: Splitting data into Train and Test Sets1:29
Implementation in Python: Pre-processing2:00
Implementation in Python: Training the model1:05
Implementation in Python: Results prediction & Confusion matrix2:23
Logistic Regression vs Linear Regression2:26
Section 08: Clustering

Introduction to clustering0:53
Use cases0:59
K-Means Clustering Algorithm1:26
Elbow method1:35
Steps of the Elbow method1:11
Implementation in python4:15
Hierarchical clustering1:17
Density-based clustering1:35
Implementation of k-means clustering in Python1:03
Importing the dataset3:06
Visualizing the dataset2:20
Defining the classifier1:37
3D Visualization of the clusters1:19
Number of predicted clusters2:51
Section 09: Recommender System

Introduction S91:28
Collaborative Filtering in Recommender Systems0:42
Content-based Recommender System0:51
Implementation in Python: Importing libraries & datasets2:57
Merging datasets into one dataframe0:53
Sorting by title and rating3:40
Histogram showing number of ratings0:50
Frequency distribution1:04
Jointplot of the ratings and number of ratings1:17
Data pre-processing2:04
Sorting the most-rated movies1:00
Grabbing the ratings for two movies1:25
Correlation between the most-rated movies2:15
Sorting the data by correlation0:54
Filtering out movies0:41
Sorting values1:02
Repeating the process for another movie2:23
Section 10: Conclusion

Conclusion0:22

Python for Machine Learning: The Complete Beginner's Course

Apex Learning

4.5/5 Instructor Rating: ★ ★ ★ ★ ★ ★

Apex Learning aims to be an online education that provides the necessary support to the learners and facilitates educators' work and contributes to professional development. They offer innovative pedagogical resources and training in a planned, updated, and oriented way.

Description

Create Machine Learning Algorithms in Python

Unlock the power of Python to master machine learning in this comprehensive beginner's course! Learn key concepts, from data preprocessing to building and evaluating models, with hands-on projects to cement your skills. Perfect for anyone new to coding or data science, this course equips you with practical knowledge to confidently start your machine learning journey. No prior experience is needed!

4.3/5 average rating: ★ ★ ★ ★ ★ ★

What you'll learn

Access 85 lectures & 2 hours of content 24/7
Learn Python programming & apply Scikit-learn to machine learning regression
Understand the underlying theory behind simple & multiple linear regression techniques
Solve regression problems (linear regression & logistic regression)
Understand both the theory and practical application of logistic regression using sklearn.
Learn the mathematics behind decision trees
Learn about the different algorithms for clustering

Who this course is for

Anyone who wants to pursue a career in Machine Learning
Any Python programming enthusiast willing to add machine learning proficiency to their portfolio
Technologists who are curious about how Machine Learning works in the real world
Programmers who are looking to add machine learning to their skillset

Specs

Important Details

Length of time users can access this course: lifetime
Access options: desktop & mobile
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels
Certificate of Completion ONLY
Updates included
Closed captioning NOT available
NOT downloadable for offline viewing
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Requirements

Any device with basic specifications
Experience with the basics of Python
Basic mathematical skills

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