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 is it 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
  • ANOVA
  • 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
  • MNIST
  • Deep Learning Applications

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Who should attend this Python with Machine Learning Training Course?

The Python with Machine Learning Course is for those looking to enhance their understanding of Machine Learning using Python, particularly for developing complex algorithms. The Python with Machine Learning Course can benefit the following individuals:

  • 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 Course.

Machine Learning with Python Training Course Overview

Machine Learning with Python influences artificial intelligence to enable computers to learn and develop autonomously without direct programming. Flexible and widely adopted, Python offers a rich array of libraries and tools specifically tailored for machine learning tasks. This training empowers organisations to train their teams to effectively leverage data, build predictive models, and make informed, data-driven decisions.

This Python with Machine Learning Course allows individuals to address challenges and innovate using data-driven solutions, significantly contributing to their organisations. Pursuing this training equips individuals with a highly sought-after skill set across various sectors, enhancing job opportunities in roles such as Data Scientists, Software Developers, and Business Analysts.

In this 2-day Python with Machine Learning Course, by The Knowledge Academy, delegates will learn about practical applications and use cases of Machine Learning. They will explore evaluation metrics and algorithms like K-means clustering, decision trees, and neural networks. The training covers various machine learning models such as regression, classification, unsupervised, and deep learning. Led by an experienced trainer specialising in Machine Learning, delegates will gain comprehensive knowledge and hands-on skills in this field.

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 completing this training, delegates can construct and assess Machine Learning models using Python. They will be proficient in selecting suitable algorithms for various problems and adept at utilising Jupyter Notebook for model development and presentation. Furthermore, they will be equipped to tackle real-world challenges using effective 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
Hands-On Labs: Included as part of our online instructor-led delivery, these labs provide real-world exercises in a simulated environment guided by expert instructors to enhance your practical skills.

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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.

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Live classes

Join a scheduled class with a live instructor and other delegates.

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Engage in activities, and communicate with your trainer and peers.

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Global Pool of the Best Trainers

We handpick from a global pool of expert trainers for our Online Instructor-led courses.

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With 10+ years of quality, instructor-led training, we equip professionals with lasting skills for success.

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With classes running in all timezones, access any of our courses and course material from anywhere, anytime.

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.

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Highly experienced trainers

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

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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

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Small class sizes

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

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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.

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Tailored learning experience

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

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Maximise your training budget

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

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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

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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 universal programming language used in web development, data analysis, artificial intelligence, machine learning, scientific computing, game development, and more. Its popularity stems from its simplicity and ease of use and from the 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, a vast range of libraries and frameworks, and strong community support. It allows developers to quickly and easily prototype and deploy machine learning models.
This Python with Machine Learning Course is a 2-day training course during which delegates participate in intensive learning sessions that cover various course topics.
There are no formal prerequisites for this Python with Machine Learning Course.
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 Python with Machine Learning Course, you can build machine learning models to solve real-world problems.
In this Python with Machine Learning Course, you will understand various machine learning models, including regression, classification, unsupervised learning, and deep learning.
There is no exam at the end of the Python with Machine Learning Course.
Yes, the Python with Machine Learning Course can help you transition into a tech role. It provides essential Python programming and machine learning skills, highly valued in the technology industry.
Python offers robust job opportunities, particularly in data science, machine learning, web development, and automation. Its versatility and readability make it popular across various industries, alongside languages like Java, JavaScript, and C#. Specific job demand often depends on industry trends and project requirements.
Yes, Python with Machine Learning Training can help optimise business processes by enabling professionals to leverage data effectively, build predictive models, automate tasks, and make data-driven decisions, enhancing efficiency and effectiveness across various business functions.
Industries such as technology, finance, healthcare, e-commerce, manufacturing, marketing, and telecommunications can most benefit from Python with Machine Learning Certification.
The Knowledge Academy in Colombia stands out as a prestigious training provider known for its extensive course offerings, expert instructors, adaptable learning formats, and industry recognition. It's a dependable option for those seeking Python with Machine Learning Certification.
The training fees for Python with Machine Learning Trainingin Colombia starts from $2295
The Knowledge Academy is the Leading global training provider for Python with Machine Learning Training.
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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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