Python for Machine Learning: The Complete Beginner's Course

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Python for Machine Learning

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Imperial Academy offers this premium Python for Machine Learning: The Complete Beginner’s Course course to ensure security in your career. In addition, this comprehensive Python for Machine Learning: The Complete Beginner’s Course course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Python for Machine Learning: The Complete Beginner’s Course will put your head and shoulder above others in front of potential employers.

Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Python for Machine Learning: The Complete Beginner’s Course!

Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.

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 00:01:00
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 00:05:00
Implementation in Python: Splitting data into Train and Test Sets 00:02:00
Implementation in Python: Training the model on the Training set 00:01:00
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 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 00:02:00
Elbow method 00:02:00
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 00:01:00
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 00:04:00
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

Certificate of Achievement

Learners will get an certificate of achievement directly at their doorstep after successfully completing the course!

It should also be noted that international students must pay £10 for shipping cost.

CPD Accredited Certification

Upon successfully completing the course, you will be qualified for CPD Accredited Certificate. Certification is available –

Course Info

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  • IT & Software
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  • £30
  • 1 year
  • Number of Units85
  • Number of Quizzes0
  • 2 hours, 32 minutes

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