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Machine Learning with Python Overview

Machine Learning with Python Course is yet another “Teacher’s Choice” course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Machine Learning with Python Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.

Consisting of several modules, the course teaches you everything you need to succeed in this profession.

The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.

Why Choose Teachers Training

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Entry Requirements

No formal entry requirements. You need to have:

Certification

CPD Certification from The Teachers Training

Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.

  • You can get your digital certificate (PDF) for £4.99 only
  • Hard copy certificates are also available, and you can get one for only £10.99
  • You can get both PDF and Hard copy certificates for just £12.99!

The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.

Course Curriculum

Machine Learning with Python
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 S2 00:01:00
Python Libraries for Machine Learning 00:02:00
Setting up Python 00:02:00
What is Jupyter? 00:02:00
Anaconda Installation Windows Mac and Ubuntu 00:04:00
Implementing Python in Jupyter 00:01:00
Managing Directories in Jupyter Notebook 00:03:00
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 00:02:00
Implementation in Python: Distribution of the data 00:02:00
Implementation in Python: Creating a linear regression object 00:03:00
Section 04: Multiple Linear Regression
Understanding Multiple linear regression 00:02:00
Implementation in Python: Exploring the dataset 00:04:00
Implementation in Python: Encoding Categorical Data 00:03:00
Implementation in Python: Splitting data into Train and Test Sets 00:02:00
Implementation in Python: Training the model on the Training set 00:02:00
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 00:01:00
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 00:01:00
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? 00:01:00
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 00:03:00
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 S7 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 00:02:00
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
Steps of the Elbow method 00:01:00
Steps of the Elbow method 00:01:00
Implementation in python 00:04:00
Hierarchical clustering 00:01:00
Density-based clustering 00:02:00
Implementation of k-means clustering in Python 00:01:00
Importing the dataset 00:03:00
Visualizing the dataset 00:02:00
Defining the classifier 00:02:00
3D Visualization of the clusters 00:03:00
Number of predicted clusters 00:02:00
Section 09: Recommender System
Introduction S9 00:02:00
Collaborative Filtering in Recommender Systems 00:01:00
Content-based Recommender System 00:01:00
Implementation in Python: Importing libraries & datasets 00:03:00
Merging datasets into one dataframe 00:01:00
Sorting by title and rating 00:01:00
Histogram showing number of ratings 00:01:00
Frequency distribution 00:01:00
Jointplot of the ratings and number of ratings 00:01:00
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 00:02:00
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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