Machine Learning With Python Training | Programming Training in Bandar Seri Begawan

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

Who Should Attend this Python with Machine Learning 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 course can benefit the following professionals:

• 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.

Python with Machine Learning Course Overview

Python with Machine Learning Course introduces delegates to Artificial Intelligence concepts that enable systems to learn from data without explicit programming. The training focuses on using Python libraries and tools to build predictive models and support data-driven decision-making.

This training supports upskilling by strengthening analytical thinking, model development, and data interpretation skills. Delegates enhance their ability to apply machine learning techniques across sectors, supporting innovation and expanding career opportunities in data-focused roles.

This 2-Day Python with Machine Learning Course offered by The Knowledge Academy helps delegates apply machine learning principles confidently in real-world scenarios. Delegates gain practical experience with algorithms, evaluation metrics, and regression, classification, and deep learning models.

Python with Machine Learning 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 Python with Machine Learning Course, 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.

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