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Python for Machine Learning: The Complete Beginner's Course

Overview This course explores Python programming for machine learning and is suitable for beginners. You will discover how to create …

Python for Machine Learning The Complete Beginner's Course

Python for Machine Learning: The Complete Beginner's Course

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Python for Machine Learning: The Complete Beginner’s Course Overview

This course explores Python programming for machine learning and is suitable for beginners. You will discover how to create your own development environment and investigate basic machine learning algorithms.  In order to make predictions, we’ll explore classification algorithms like K-Nearest Neighbours, Decision Trees, and Logistic Regression in addition to linear regression for relationship analysis.  Recommender system concepts and data clustering techniques are also on the agenda.  By the end, you’ll understand the fundamentals of machine learning and have a strong foundation in Python.

Learning Outcomes

  • Understand the principles of machine learning.
  • Create a Python environment in order to develop machine learning.
  • Use Python to implement both single and multiple linear regression.
  • K-Nearest Neighbours can be used for classification tasks.
  • Construct decision trees to aid in forecasting.
  • For problems involving classification, use logistic regression.
  • Use clustering algorithms to efficiently organise data.
  • Recognise the ideas underlying recommender systems.
  • To apply machine learning algorithms, write Python code.
  • Obtain the self-assurance to investigate additional machine learning subjects.

Who Is This Course For

This course is intended for people who want to learn Python for creating machine learning models but have no prior experience with the field.  This course serves as a starting point for anyone interested in machine learning, be they a student, professional looking to change careers, or someone just curious about AI.

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Numerous interesting career paths are accessible with a solid foundation in Python and machine learning.  Using Python, you could develop and implement machine learning models in your career as a machine learning engineer.  Python is used by data scientists to analyse data and develop machine learning algorithms.  You will graduate from this course with the necessary skills to launch a successful career in this in-demand industry.

 

Course Curriculum

Section 01: Introduction to Machine Learning
What is Machine Learning? 00:02:00
Applications of Machine Learning 00:02:00
Machine learning Methods 00:01:00
What is Supervised learning? 00:01:00
What is Unsupervised learning? 00:01:00
Supervised learning vs Unsupervised learning 00:04:00
Section 02: Setting Up Python & ML Algorithms Implementation
Introduction 00:03:00
Python Libraries for Machine Learning 00:02:00
Setting up Python 00:02:00
What is Jupyter? 00:05:00
Anaconda Installation Windows Mac and Ubuntu 00:06:00
Implementing Python in Jupyter 00:01:00
Managing Directories in Jupyter Notebook 00:05:00
Section 03: Simple Linear Regression
Introduction to regression 00:02:00
How Does Linear Regression Work? 00:02:00
Line representation 00:01:00
Implementation in Python: Importing libraries & datasets 00:05:00
Implementation in Python: Distribution of the data 00:02:00
Implementation in Python: Creating a linear regression object 00:05:00
Section 04: Multiple Linear Regression
Understanding Multiple linear regression 00:03:00
Implementation in Python: Exploring the dataset 00:00:00
Implementation in Python: Encoding Categorical Data 00:06:00
Implementation in Python: Splitting data into Train and Test Sets 00:04:00
Implementation in Python: Training the model on the Training set 00:04:00
Implementation in Python: Predicting the Test Set results 00:03:00
Evaluating the performance of the regression model 00:01:00
Root Mean Squared Error in Python 00:03:00
Section 05: Classification Algorithms: K-Nearest Neighbors
Introduction to classification 00:01:00
K-Nearest Neighbors algorithm 00:01:00
Example of KNN 00:03:00
K-Nearest Neighbours (KNN) using python 00:01:00
Implementation in Python: Importing required libraries 00:01:00
Implementation in Python: Importing the dataset 00:02:00
Implementation in Python: Splitting data into Train and Test Sets 00:03:00
Implementation in Python: Feature Scaling 00:03:00
Implementation in Python: Importing the KNN classifier 00:02:00
Implementation in Python: Results prediction & Confusion matrix 00:02:00
Section 06: Classification Algorithms: Decision Tree
Introduction to decision trees 00:01:00
What is Entropy? 00:02:00
Exploring the dataset 00:01:00
Decision tree structure 00:01:00
Implementation in Python: Importing libraries & datasets 00:01:00
Implementation in Python: Encoding Categorical Data 00:05:00
Implementation in Python: Splitting data into Train and Test Sets 00:01:00
Implementation in Python: Results Prediction & Accuracy 00:03:00
Section 07: Classification Algorithms: Logistic regression
Introduction 00:01:00
Implementation steps 00:01:00
Implementation in Python: Importing libraries & datasets 00:02:00
Implementation in Python: Splitting data into Train and Test Sets 00:01:00
Implementation in Python: Pre-processing 00:02:00
Implementation in Python: Training the model 00:01:00
Implementation in Python: Results prediction & Confusion matrix 00:02:00
Logistic Regression vs Linear Regression 00:02:00
Section 08: Clustering
Introduction to clustering 00:01:00
Use cases 00:01:00
K-Means Clustering Algorithm 00:01:00
Elbow method 00:04:00
Steps of the Elbow method 00:01:00
Implementation in python 00:07:00
Hierarchical clustering 00:02:00
Density-based clustering 00:03:00
Implementation of k-means clustering in Python 00:03:00
Importing the dataset 00:04:00
Visualizing the dataset 00:03:00
Defining the classifier 00:02:00
3D Visualization of the clusters 00:04:00
Number of predicted clusters 00:02:00
Section 09: Recommender System
Introduction 00:02:00
Collaborative Filtering in Recommender Systems 00:01:00
Content-based Recommender System 00:02:00
Implementation in Python: Importing libraries & datasets 00:05:00
Merging datasets into one dataframe 00:00:00
Sorting by title and rating 00:04:00
Histogram showing number of ratings 00:01:00
Frequency distribution 00:03:00
Jointplot of the ratings and number of ratings 00:02:00
Data pre-processing 00:02:00
Sorting the most-rated movies 00:01:00
Grabbing the ratings for two movies 00:01:00
Correlation between the most-rated movies 00:00:00
Sorting the data by correlation 00:01:00
Filtering out movies 00:01:00
Sorting values 00:01:00
Repeating the process for another movie 00:02:00
Section 10: Conclusion
Conclusion 00:01:00

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