Python with Machine Learning Training Overview

Machine Learning with Python Training Course Outline

Module 1: Introduction to Machine Learning 

  • What is Machine Learning?
  • Python for Machine Learning 
  • AI vs Machine Learning
  • Classification of Machine Learning
  • Supervised vs Unsupervised Learning
  • Reinforcement Learning
  • Datasets for ML 
  • Popular Sources of ML Datasets
  • Kaggle Datasets
  • UCI Machine Learning Repository
  • Datasets via AWS
  • Google’s Datasets Search Engine
  • Microsoft Datasets
  • Computer Vision Datasets
  • Scikit-learn Datasets
  • Application of Machine Learning 
  • Virtual Process Assistance
  • Email Spam and Malware Filtering
  • Traffic Prediction
  • Image Recognition
  • Speech Recognition
  • Product Recommendation
  • Self-Driving Car
  • Detection Online Frauds
  • Python libraries for Machine Learning
  • Numpy
  • Pandas
  • Matplotib
  • Scikit-learn
  • Scipy
  • Tensorflow
  • Pytorch
  • Keras

Module 2: Regression 

  • Introduction to Regression 
  • Why do we use Regression Analysis?
  • ​Regression Analysis-Related Terminologies​
  • Types of Regression​
  • Linear Regression
  • Linear Regression Formula​
  • Types of Linear Regression​
  • Linear Regression Line​
  • Polynomial Regression​
  • Non-Linear Regression 
  • Model Evaluation Process
  • Cross Validation in ML
  • Methods Used for Cross-Validation​
  • Types of Predictive Model​
  • Confusion Matrix​
  • Area Under the ROC Curve (AUC-ROC) ​
  • ROC Curve​
  • AUC Curve​
  • Application of AUC-ROC Curve​
  • Mean Squared Error (MSE)​
  • Root Mean Squared Error (RMSE)​
  • K-fold Cross Validation​
  • Hands-On Linear Regression ​

Module 3: Classification 

  • Introduction to Classification
  • Classifier​ 
  • K-Nearest Neighbours
  • How KNN works? ​
  • Decision Tree​
  • Why to Use Decision Tree? ​
  • Decision Tree Terminologies​
  • Decision Tree Steps​
  • Advantages and Disadvantages (Decision Tree) ​
  • Logistic Regression​
  • Logistic Function (Sigmoid Function) ​
  • Equation of Logistic Regression​
  • Types of Logistic Regression​
  • Support Vector Machine (SVM)​
  • Why it is called Naive Bayes? ​
  • Bayes Theorem​
  • Advantages and Disadvantages of NB classifier ​
  • Types of Naive Bayes Model​
  • Random Forest Classification​
  • Why Random Forest ​
  • Application of Random Forest Classification​
  • Advantages and Disadvantages of RF​
  • Hands-On Logistic Regression ​

Module 4: Unsupervised Learning 

  • Introduction to Unsupervised Learning 
  • Types of Unsupervised Algorithm​
  • Advantages and Disadvantages of UL
  • Unsupervised Learning Algorithms​
  • K-Means Clustering 
  • Steps for K-means Clustering​
  • Elbow Method​
  • Hierarchical Clustering ​
  • Why Hierarchical Clustering ​
  • Density Based Clustering (DBSCAN)​
  • Apriori Algorithm​
  • Components of Apriori Algorithm​
  • Hands-On Clustering ​

Module 5: Dimensionality Reduction 

  • Dimensionality Reduction
  • Need of Dimensionality Reduction
  • Types of Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Steps for PCA Algorithm
  • What is Variance?
  • What is Covariance?
  • What is Correlation?
  • Application of PCA
  • What is P-Value?
  • Hypothesis Testing
  • Hypothesis in Statistics
  • Critical Values
  • Z Test
  • Chi-Square Test
  • Normal Distribution
  • Statistical Significance
  • Errors in P-value
  • Linear Discriminant Analysis (LDA)
  • Working of Linear Discriminant Analysis
  • How to Prepare Data for LDA
  • Real World Application of LDA
  • Difference Between PCA and LDA
  • Overfitting and Underfitting in ML
  • How to Avoid the Overfitting in the Model
  • Hands-On PCA

Module 6: Deep Learning 

  • Introduction to Deep Learning
  • Importance of Deep Learning
  • Neural Network Architecture
  • Neural Network Components
  • Neural Network Algorithms
  • Convolutional Neural Networks (CNNs)
  • Long Short Memory Network (LSTMs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)
  • Radial Basis Function Networks (RBFN)
  • Multilayer Perceptrons (MLPs)
  • Self-Organising Maps (SOMs)
  • Deep Belief Networks (DBNs)
  • Restricted Boltzmann Machine (RBMs)
  • Artificial Neural Networks (ANNs)
  • Feed Forward Neural Network
  • Autoencoders
  • Deep Learning Applications

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Who should attend this Python with Machine Learning Training Course?

The Python with Machine Learning Training Course is designed for those who want to get better at understanding the intricacies surrounding Machine Learning through Python for complex algorithm development. The Python with Machine Learning Training Course can benefit individuals such as:

  • Data Scientists
  • Software Developers
  • Data Analysts
  • Business Analysts
  • Statisticians
  • Ethical Hackers
  • Cyber Security Professionals   

Prerequisites of the Python with Machine Learning Training Course

There are no formal prerequisites for this Python with Machine Learning Training Course.

Machine Learning with Python Training Course Overview

Machine Learning with Python is an artificial intelligence application that allows computer systems to learn and develop without being explicitly designed. Python, as a flexible and widely used programming language, has an extensive set of libraries and tools created expressly for machine learning applications. This training enables organisations to equip their employees to successfully exploit data, construct predictive models, and drive data-driven decision-making. Additionally, this training will assist individuals in tackling challenges and innovating with data-driven solutions, thus making a big contribution to their organisations. Pursuing this training provides individuals with a useful skill set that is in high demand across sectors and improves their job chances, allowing them to work as data scientists, machine learning engineers, and AI specialists.

In this 2-day Machine Learning with Python training, delegates will gain in-depth knowledge about the use cases and applications of Machine Learning. During this training, delegates will learn about different evaluation metrics and algorithms, such as K-means clustering, decision trees, and neural networks. They will gain an understanding of various machine learning models, including regression, classification, unsupervised learning, and deep learning. This course will be led by our highly skilled and knowledgeable trainer, who has years of experience in teaching Machine Learning Training courses.


Course Objectives:

  • To learn Machine Learning with Python and its applications in various domains
  • To understand the different types of machine learning algorithms
  • To implement machine learning models using Python libraries and tools
  • To acquire knowledge of model evaluation techniques, including cross-validation, confusion matrix, and ROC curve analysis
  • To master the deep learning and its algorithms, such as CNNs, LSTMs, and GANs
  • To explore the field of statistical analysis and hypothesis testing for data validation and decision-making

After attending this training, delegates will be able to build and evaluate machine learning models using Python. They will be able to apply appropriate algorithms for different types of problems, and use Jupyter Notebook for model development and presentation. They will also be able to solve real-world problems using machine learning techniques.

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What’s included in this Introduction to Python with Machine Learning Training Course?

  • World-Class Training Sessions from Experienced Instructors
  • Python with Machine Learning Certificate
  • Digital Delegate Pack

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Why choose us

Ways to take this course

Experience live, interactive learning from home with The Knowledge Academy's Online Instructor-led Python with Machine Learning Training. Engage directly with expert instructors, mirroring the classroom schedule for a comprehensive learning journey. Enjoy the convenience of virtual learning without compromising on the quality of interaction.

Unlock your potential with The Knowledge Academy's Python with Machine Learning Training, accessible anytime, anywhere on any device. Enjoy 90 days of online course access, extendable upon request, and benefit from the support of our expert trainers. Elevate your skills at your own pace with our Online Self-paced sessions.

Experience the most sought-after learning style with The Knowledge Academy's Python with Machine Learning Training. Available in 490+ locations across 190+ countries, our hand-picked Classroom venues offer an invaluable human touch. Immerse yourself in a comprehensive, interactive experience with our expert-led Python with Machine Learning Training sessions.


Highly experienced trainers

Boost your skills with our expert trainers, boasting 10+ years of real-world experience, ensuring an engaging and informative training experience


State of the art training venues

We only use the highest standard of learning facilities to make sure your experience is as comfortable and distraction-free as possible


Small class sizes

Our Classroom courses with limited class sizes foster discussions and provide a personalised, interactive learning environment


Great value for money

Achieve certification without breaking the bank. Find a lower price elsewhere? We'll match it to guarantee you the best value

Streamline large-scale training requirements with The Knowledge Academy’s In-house/Onsite Python with Machine Learning Training at your business premises. Experience expert-led classroom learning from the comfort of your workplace and engage professional development.


Tailored learning experience

Leverage benefits offered from a certification that fits your unique business or project needs


Maximise your training budget

Cut unnecessary costs and focus your entire budget on what really matters, the training.


Team building opportunity

Our Python with Machine Learning Training offers a unique chance for your team to bond and engage in discussions, enriching the learning experience beyond traditional classroom settings


Monitor employees progress

The course know-how will help you track and evaluate your employees' progression and performance with relative ease

What our customers are saying

Python with Machine Learning Training FAQs

Python is a versatile programming language used in web development, data analysis, artificial intelligence, machine learning, scientific computing, game development, and more. Its popularity stems from its simplicity, ease of use, and wide range of libraries and frameworks that cater to different industries and domains.
Python is a popular language for machine learning due to its simplicity, ease of use, vast range of libraries and frameworks, and strong community support. It allows developers to quickly and easily prototype and deploy machine learning models.
Machine Learning with Python is used in various real-world applications, such as image and speech recognition, fraud detection, recommendation systems, and self-driving cars. With the skills you will learn in this course, you will be able to build Machine Learning models that can solve real-world problems.
Businesses can benefit from machine learning in various ways, such as improving decision-making, enhancing customer experience, optimising operations, detecting fraud and anomalies, and predicting trends and outcomes, leading to increased efficiency, productivity, and revenue.
In this Machine Learning with Python course, you will learn the fundamentals of machine learning and its various applications, such as virtual process assistance, email spam filtering, traffic recognition, speech recognition, self-driving cars, and other related topics.
The training fees for Python with Machine Learning Training certification in the United Kingdom starts from £1795
The Knowledge Academy is the Leading global training provider for Python with Machine Learning Training.
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Why choose us


Best price in the industry

You won't find better value in the marketplace. If you do find a lower price, we will beat it.


Many delivery methods

Flexible delivery methods are available depending on your learning style.


High quality resources

Resources are included for a comprehensive learning experience.

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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

Joshua Davies, Thames Water

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