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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

1 day, 23 hours

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

  • Share

Overview

This comprehensive bootcamp is your complete roadmap to becoming proficient in Python machine learning, from the fundamentals of data handling to the advanced stages of deep learning and natural language processing. You’ll start with a practical introduction to machine learning concepts, setting up Anaconda, Python, and Jupyter Notebook across different operating systems. This ensures your development environment is ready to handle real-world data science projects.

Next, you’ll strengthen your Python foundations with a focused crash course covering arithmetic operations, data types, control flow, functions, and file handling. You’ll then progress to NumPy for high-performance numerical computing and Pandas for powerful data manipulation, learning to clean, transform, slice, and analyse datasets effectively. The course also includes hands-on projects with stock data and other real-world datasets to help you build practical experience.

From there, you’ll master data visualisation techniques using Matplotlib, Seaborn, Plotly, and Pandas to create clear, interactive, and insightful charts. Once your data skills are strong, you’ll dive into supervised and unsupervised machine learning algorithms such as linear and logistic regression, SVM, decision trees, random forests, boosting, and clustering techniques including K-Means, DBSCAN, spectral, and hierarchical clustering. You’ll learn cross-validation, hyperparameter tuning, and PCA for dimensionality reduction, all with hands-on coding exercises.

Finally, you’ll explore deep learning fundamentals with TensorFlow, covering neural networks, activation functions, optimisers, and model evaluation. You’ll build and train deep learning models, apply them to customer churn prediction, and save and deploy your models. The course also introduces natural language processing, feature engineering, and text classification using Bag of Words and TF-IDF, equipping you with a broad skill set to handle a wide range of data science and machine learning problems. By the end of this bootcamp, you’ll have built a strong portfolio of projects and gained the confidence to apply machine learning techniques to business and research problems.

This course is ideal for beginners looking to start a career in data science and machine learning, Python programmers who want to upskill into machine learning, and professionals from fields such as business, finance, or engineering who wish to add predictive analytics and AI capabilities to their toolkit.
No prior experience in machine learning is required, but basic familiarity with Python is helpful. All installation steps, libraries, and tools are covered in detail so you can follow along smoothly regardless of your operating system or background.
After completing this bootcamp, you’ll be ready to pursue roles such as machine learning engineer, data scientist, data analyst, or AI developer. You’ll have the practical skills to clean and analyse data, build and evaluate predictive models, create visualisations, and deploy deep learning and NLP solutions, making you highly competitive in today’s data-driven job market.

Who is this course for?

This comprehensive bootcamp is your complete roadmap to becoming proficient in Python machine learning, from the fundamentals of data handling to the advanced stages of deep learning and natural language processing. You’ll start with a practical introduction to machine learning concepts, setting up Anaconda, Python, and Jupyter Notebook across different operating systems. This ensures your development environment is ready to handle real-world data science projects.

Next, you’ll strengthen your Python foundations with a focused crash course covering arithmetic operations, data types, control flow, functions, and file handling. You’ll then progress to NumPy for high-performance numerical computing and Pandas for powerful data manipulation, learning to clean, transform, slice, and analyse datasets effectively. The course also includes hands-on projects with stock data and other real-world datasets to help you build practical experience.

From there, you’ll master data visualisation techniques using Matplotlib, Seaborn, Plotly, and Pandas to create clear, interactive, and insightful charts. Once your data skills are strong, you’ll dive into supervised and unsupervised machine learning algorithms such as linear and logistic regression, SVM, decision trees, random forests, boosting, and clustering techniques including K-Means, DBSCAN, spectral, and hierarchical clustering. You’ll learn cross-validation, hyperparameter tuning, and PCA for dimensionality reduction, all with hands-on coding exercises.

Finally, you’ll explore deep learning fundamentals with TensorFlow, covering neural networks, activation functions, optimisers, and model evaluation. You’ll build and train deep learning models, apply them to customer churn prediction, and save and deploy your models. The course also introduces natural language processing, feature engineering, and text classification using Bag of Words and TF-IDF, equipping you with a broad skill set to handle a wide range of data science and machine learning problems. By the end of this bootcamp, you’ll have built a strong portfolio of projects and gained the confidence to apply machine learning techniques to business and research problems.

This course is ideal for beginners looking to start a career in data science and machine learning, Python programmers who want to upskill into machine learning, and professionals from fields such as business, finance, or engineering who wish to add predictive analytics and AI capabilities to their toolkit.
No prior experience in machine learning is required, but basic familiarity with Python is helpful. All installation steps, libraries, and tools are covered in detail so you can follow along smoothly regardless of your operating system or background.
After completing this bootcamp, you’ll be ready to pursue roles such as machine learning engineer, data scientist, data analyst, or AI developer. You’ll have the practical skills to clean and analyse data, build and evaluate predictive models, create visualisations, and deploy deep learning and NLP solutions, making you highly competitive in today’s data-driven job market.

Requirements

This comprehensive bootcamp is your complete roadmap to becoming proficient in Python machine learning, from the fundamentals of data handling to the advanced stages of deep learning and natural language processing. You’ll start with a practical introduction to machine learning concepts, setting up Anaconda, Python, and Jupyter Notebook across different operating systems. This ensures your development environment is ready to handle real-world data science projects.

Next, you’ll strengthen your Python foundations with a focused crash course covering arithmetic operations, data types, control flow, functions, and file handling. You’ll then progress to NumPy for high-performance numerical computing and Pandas for powerful data manipulation, learning to clean, transform, slice, and analyse datasets effectively. The course also includes hands-on projects with stock data and other real-world datasets to help you build practical experience.

From there, you’ll master data visualisation techniques using Matplotlib, Seaborn, Plotly, and Pandas to create clear, interactive, and insightful charts. Once your data skills are strong, you’ll dive into supervised and unsupervised machine learning algorithms such as linear and logistic regression, SVM, decision trees, random forests, boosting, and clustering techniques including K-Means, DBSCAN, spectral, and hierarchical clustering. You’ll learn cross-validation, hyperparameter tuning, and PCA for dimensionality reduction, all with hands-on coding exercises.

Finally, you’ll explore deep learning fundamentals with TensorFlow, covering neural networks, activation functions, optimisers, and model evaluation. You’ll build and train deep learning models, apply them to customer churn prediction, and save and deploy your models. The course also introduces natural language processing, feature engineering, and text classification using Bag of Words and TF-IDF, equipping you with a broad skill set to handle a wide range of data science and machine learning problems. By the end of this bootcamp, you’ll have built a strong portfolio of projects and gained the confidence to apply machine learning techniques to business and research problems.

This course is ideal for beginners looking to start a career in data science and machine learning, Python programmers who want to upskill into machine learning, and professionals from fields such as business, finance, or engineering who wish to add predictive analytics and AI capabilities to their toolkit.
No prior experience in machine learning is required, but basic familiarity with Python is helpful. All installation steps, libraries, and tools are covered in detail so you can follow along smoothly regardless of your operating system or background.
After completing this bootcamp, you’ll be ready to pursue roles such as machine learning engineer, data scientist, data analyst, or AI developer. You’ll have the practical skills to clean and analyse data, build and evaluate predictive models, create visualisations, and deploy deep learning and NLP solutions, making you highly competitive in today’s data-driven job market.

Career path

This comprehensive bootcamp is your complete roadmap to becoming proficient in Python machine learning, from the fundamentals of data handling to the advanced stages of deep learning and natural language processing. You’ll start with a practical introduction to machine learning concepts, setting up Anaconda, Python, and Jupyter Notebook across different operating systems. This ensures your development environment is ready to handle real-world data science projects.

Next, you’ll strengthen your Python foundations with a focused crash course covering arithmetic operations, data types, control flow, functions, and file handling. You’ll then progress to NumPy for high-performance numerical computing and Pandas for powerful data manipulation, learning to clean, transform, slice, and analyse datasets effectively. The course also includes hands-on projects with stock data and other real-world datasets to help you build practical experience.

From there, you’ll master data visualisation techniques using Matplotlib, Seaborn, Plotly, and Pandas to create clear, interactive, and insightful charts. Once your data skills are strong, you’ll dive into supervised and unsupervised machine learning algorithms such as linear and logistic regression, SVM, decision trees, random forests, boosting, and clustering techniques including K-Means, DBSCAN, spectral, and hierarchical clustering. You’ll learn cross-validation, hyperparameter tuning, and PCA for dimensionality reduction, all with hands-on coding exercises.

Finally, you’ll explore deep learning fundamentals with TensorFlow, covering neural networks, activation functions, optimisers, and model evaluation. You’ll build and train deep learning models, apply them to customer churn prediction, and save and deploy your models. The course also introduces natural language processing, feature engineering, and text classification using Bag of Words and TF-IDF, equipping you with a broad skill set to handle a wide range of data science and machine learning problems. By the end of this bootcamp, you’ll have built a strong portfolio of projects and gained the confidence to apply machine learning techniques to business and research problems.

This course is ideal for beginners looking to start a career in data science and machine learning, Python programmers who want to upskill into machine learning, and professionals from fields such as business, finance, or engineering who wish to add predictive analytics and AI capabilities to their toolkit.
No prior experience in machine learning is required, but basic familiarity with Python is helpful. All installation steps, libraries, and tools are covered in detail so you can follow along smoothly regardless of your operating system or background.
After completing this bootcamp, you’ll be ready to pursue roles such as machine learning engineer, data scientist, data analyst, or AI developer. You’ll have the practical skills to clean and analyse data, build and evaluate predictive models, create visualisations, and deploy deep learning and NLP solutions, making you highly competitive in today’s data-driven job market.

    • Course Introduction 00:10:00
    • Machine Learning Introduction 00:10:00
    • Install Anaconda and Python on Windows 00:10:00
    • Install Anaconda on Linux 00:10:00
    • Jupyter Notebook Introduction and Keyboard Shortcuts 00:10:00
    • Arithmetic Operations in Python 00:10:00
    • Data Types in Python 00:10:00
    • Variable Casting 00:10:00
    • String Operations in Python 00:10:00
    • String Slicing in Python 00:10:00
    • String Formatting and Modification 00:10:00
    • Boolean Variables and Evaluation 00:10:00
    • Lists in Python 00:10:00
    • Tuples in Python 00:10:00
    • 10: Sets 00:10:00
    • Dictionaries 00:10:00
    • Conditional Statements – If Else 00:10:00
    • While Loops 00:10:00
    • For Loops 00:10:00
    • Functions 00:10:00
    • Working with Date and Time 00:10:00
    • File Handling – Read and Write 00:10:00
    • NumPy Introduction – Create NumPy Arrays 00:10:00
    • Array Indexing and Slicing 00:10:00
    • NumPy Data Types 00:10:00
    • Handling np.nan and np.inf 00:10:00
    • Statistical Operations 00:10:00
    • shape(), reshape(), ravel(), flatten() 00:10:00
    • arange(), linspace(), range(), random(), zeros(), and ones() 00:10:00
    • np.where 00:10:00
    • NumPy Array Read and Write 00:10:00
    • Pandas Series Introduction Part 1 00:10:00
    • Pandas Series Introduction Part 2 00:10:00
    • Reading Series from File 00:10:00
    • Applying Python Built-in Functions to Series 00:10:00
    • Using apply() with Pandas Series 00:10:00
    • Creating Pandas DataFrame from Scratch 00:10:00
    • Reading Files as DataFrames 00:10:00
    • Column Manipulation Part 1 00:10:00
    • Column Manipulation Part 2 00:10:00
    • Arithmetic Operations 00:10:00
    • Handling NULL Values 00:10:00
    • DataFrame Filtering Part 1 00:10:00
    • DataFrame Filtering Part 2 00:10:00
    • Handling Unique and Duplicate Values 00:10:00
    • Retrieving Rows by Index Label 00:10:00
    • Replacing Cell Values 00:10:00
    • Renaming and Deleting Index and Columns 00:10:00
    • Using Lambda with apply() 00:10:00
    • Pandas GroupBy 00:10:00
    • GroupBy Multiple Columns 00:10:00
    • Merging, Joining, and Concatenation Part 1 00:10:00
    • Concatenation 00:10:00
    • Merge and Join 00:10:00
    • Working with Datetime 00:10:00
    • Reading Stock Data from Yahoo Finance 00:10:00
    • Introduction to Matplotlib 00:10:00
    • Line Plot Part 1 00:10:00
    • IMDB Movie Revenue Line Plot Part 1 00:10:00
    • IMDB Movie Revenue Line Plot Part 2 00:10:00
    • Line Plot Rank vs Runtime, Votes, Metascore 00:10:00
    • Line Styling and Adding Labels 00:10:00
    • Scatter, Bar, and Histogram Plot Part 1 00:10:00
    • Scatter, Bar, and Histogram Plot Part 2 00:10:00
    • Subplots Part 1 00:10:00
    • Subplots Part 2 00:10:00
    • Using Subplots 00:10:00
    • Creating Zoomed Sub-Figure 00:10:00
    • xlim, ylim, legend, grid, xticks, yticks 00:10:00
    • Pie Chart and Saving Figures 00:10:00
    • Seaborn Introduction 00:10:00
    • Scatter Plot 00:10:00
    • Hue, Style and Size Part 1 00:10:00
    • Hue, Style and Size Part 2 00:10:00
    • Line Plot Part 1 00:10:00
    • Line Plot Part 2 00:10:00
    • Line Plot Part 3 00:10:00
    • Using Subplots 00:10:00
    • sns.lineplot() and sns.scatterplot() 00:10:00
    • Categorical Plot (catplot) 00:10:00
    • Box Plot 00:10:00
    • Boxen Plot 00:10:00
    • Violin Plot 00:10:00
    • Bar Plot 00:10:00
    • Point Plot 00:10:00
    • Joint Plot 00:10:00
    • Pair Plot 00:10:00
    • Regression Plot 00:10:00
    • Controlling Plot Aesthetics 00:10:00
    • Introduction to the IRIS Dataset 00:10:00
    • Loading the IRIS Dataset 00:10:00
    • Line Plot 00:10:00
    • Using Secondary Axis 00:10:00
    • Bar and Horizontal Bar Plot 00:10:00
    • Stacked Bar Plot 00:10:00
    • Histogram 00:10:00
    • Box Plot 00:10:00
    • Area and Scatter Plot 00:10:00
    • Hexbin Plot 00:10:00
    • Pie Chart 00:10:00
    • Scatter Matrix and Subplots 00:10:00
    • Introduction to Plotly and Cufflinks 00:10:00
    • Plotly Line Plot 00:10:00
    • Scatter Plot 00:10:00
    • Stacked Bar Plot 00:10:00
    • Box and Area Plot 00:10:00
    • 3D Plot 00:10:00
    • Histogram, Bubble Plot, and Heatmap 00:10:00
    • Linear Regression Introduction 00:10:00
    • Regression Examples 00:10:00
    • Types of Linear Regression 00:10:00
    • Assessing Model Performance 00:10:00
    • Bias-Variance Tradeoff 00:10:00
    • Introduction to sklearn and train_test_split 00:10:00
    • Python Package Upgrade and Import 00:10:00
    • Loading Boston Housing Dataset 00:10:00
    • Dataset Analysis 00:10:00
    • Exploratory Data Analysis – Pair Plot 00:10:00
    • Exploratory Data Analysis – Histogram Plot 00:10:00
    • Exploratory Data Analysis – Heatmap 00:10:00
    • Train-Test Split and Model Training 00:10:00
    • Evaluating Regression Model Performance 00:10:00
    • Plotting True vs Predicted House Price 00:10:00
    • Plotting Learning Curves Part 1 00:10:00
    • Plotting Learning Curves Part 2 00:10:00
    • Residuals Plot for Model Interpretability 00:10:00
    • Prediction Error Plot for Interpretability 00:10:00
    • Logistic Regression Introduction 00:10:00
    • Sigmoid Function 00:10:00
    • Decision Boundary 00:10:00
    • Titanic Dataset Introduction 00:10:00
    • Dataset Loading 00:10:00
    • EDA – Heatmap and Density Plot 00:10:00
    • Missing Age Imputation Part 1 00:10:00
    • Missing Age Imputation Part 2 00:10:00
    • Imputation of Missing Embark Town 00:10:00
    • Data Types Correction and Mapping 00:10:00
    • One-Hot Encoding 00:10:00
    • Train-Test Split 00:10:00
    • Model Building, Training, and Evaluation 00:10:00
    • Feature Selection – Recursive Feature Elimination 00:10:00
    • Accuracy, F1-Score, Precision, Recall, AUC-ROC Curve Part 1 00:10:00
    • Accuracy, F1-Score, Precision, Recall, AUC-ROC Curve Part 2 00:10:00
    • Accuracy, F1-Score, Precision, Recall, AUC-ROC Curve Part 3 00:10:00
    • ROC Curve and AUC Part 1 00:10:00
    • ROC Curve and AUC Part 2 00:10:00
    • ROC Curve and AUC Part 3 00:10:00
    • SVM Introduction 00:10:00
    • SVM Kernels 00:10:00
    • Breast Cancer Dataset Introduction 00:10:00
    • Dataset Loading 00:10:00
    • Cancer Data Visualization Part 1 00:10:00
    • Cancer Data Visualization Part 2 00:10:00
    • Data Standardization 00:10:00
    • Train-Test Split 00:10:00
    • Linear SVM Model Building and Training 00:10:00
    • Linear SVM Model on Scaled Features 00:10:00
    • Polynomial, Sigmoid, and RBF Kernels in SVM 00:10:00
    • Cross Validation, Regularization, and Hyperparameter Concepts 00:10:00
    • Machine Learning Model Training Process 00:10:00
    • Breast Cancer Dataset Loading 00:10:00
    • Data Visualization 00:10:00
    • Train-Test Split 00:10:00
    • Linear Regression and SVM Model Training 00:10:00
    • Introduction to Regularization 00:10:00
    • Manual Hyperparameter Adjustment 00:10:00
    • Types of Cross Validation 00:10:00
    • K-Fold and Leave-One-Out Cross Validation 00:10:00
    • Grid Search Hyperparameter Tuning 00:10:00
    • Randomized Grid Search Hyperparameter Tuning 00:10:00
    • KNN Introduction 00:10:00
    • How KNN Works 00:10:00
    • Wine Dataset Loading 00:10:00
    • Data Visualization 00:10:00
    • Train-Test Split and Standardization 00:10:00
    • KNN Model Building and Training 00:10:00
    • Hyperparameter Tuning 00:10:00
    • Pros and Cons of KNN 00:10:00
    • Decision Tree Introduction 00:10:00
    • How Decision Trees Work 00:10:00
    • Attribute Selection Measures (ASM) 00:10:00
    • Dataset Loading 00:10:00
    • Dataset Visualization 00:10:00
    • Train-Test Split 00:10:00
    • Model Training and Evaluation 00:10:00
    • Tree Visualization 00:10:00
    • Hyperparameter Optimization 00:10:00
    • Diabetes Dataset Loading 00:10:00
    • Decision Tree Regression 00:10:00
    • Ensemble Learning: Bagging and Boosting Introduction 00:10:00
    • Random Forest Introduction 00:10:00
    • Dataset Introduction 00:10:00
    • Data Visualization 00:10:00
    • Train-Test Split and One-Hot Encoding 00:10:00
    • Random Forest Classifier Training and Evaluation 00:10:00
    • Data Loading for Random Forest Regression 00:10:00
    • Random Forest Regression Model Building 00:10:00
    • Hyperparameter Optimization 00:10:00
    • Boosting Algorithms Introduction 00:10:00
    • Heart Disease Dataset Understanding 00:10:00
    • Data Visualization Part 1 00:10:00
    • Train-Test Split 00:10:00
    • AdaBoost Model Training 00:10:00
    • AdaBoost Hyperparameter Tuning 00:10:00
    • XGBoost Introduction 00:10:00
    • XGBoost Model Training and Hyperparameter Tuning 00:10:00
    • CatBoost Model Training 00:10:00
    • CatBoost Hyperparameter Optimization 00:10:00
    • Introduction to Unsupervised Learning 00:10:00
    • K-Means Clustering Overview 00:10:00
    • Choosing the Best Number of Clusters 00:10:00
    • K-Means Clustering with Scikit-Learn 00:10:00
    • Applications of Unsupervised Learning 00:10:00
    • Customer Data Loading 00:10:00
    • Data Visualization 00:10:00
    • K-Means Clustering Data Preparation 00:10:00
    • Clustering by Age and Spending Score 00:10:00
    • Cluster Visualization 00:10:00
    • Decision Boundary Visualization 00:10:00
    • Full Clustering Workflow 00:10:00
    • Selecting the Optimal Number of Clusters 00:10:00
    • Clustering by Annual Income vs Spending Score 00:10:00
    • 3D Clustering Part 1 00:10:00
    • 3D Clustering Part 2 00:10:00
    • DBSCAN Introduction 00:10:00
    • Generating Dataset 00:10:00
    • DBSCAN Clustering 00:10:00
    • Spectral Clustering Introduction 00:10:00
    • Spectral Clustering Implementation 00:10:00
    • Hierarchical Clustering Introduction 00:10:00
    • Important Terms in Hierarchical Clustering 00:10:00
    • Stock Market Data Loading 00:10:00
    • Hierarchical Clustering Coding 00:10:00
    • PCA Introduction 00:10:00
    • How PCA Works 00:10:00
    • MNIST Dataset Loading and Understanding 00:10:00
    • PCA Applications 00:10:00
    • PCA Coding 00:10:00
    • PCA Compression Analysis 00:10:00
    • Data Reconstruction 00:10:00
    • Choosing the Right Number of Principal Components 00:10:00
    • Data Reconstruction with 95% Information 00:10:00
    • Classification Comparison With and Without PCA 00:10:00
    • What is a Neuron? 00:10:00
    • Multi-Layer Perceptron 00:10:00
    • Shallow vs Deep Neural Networks 00:10:00
    • Activation Functions 00:10:00
    • What is Backpropagation? 00:10:00
    • Optimizers in Deep Learning 00:10:00
    • Steps to Build a Neural Network 00:10:00
    • Installing TensorFlow on Windows 00:10:00
    • Installing TensorFlow on Linux 00:10:00
    • Customer Churn Dataset Loading 00:10:00
    • Data Visualization Part 1 00:10:00
    • Data Visualization Part 2 00:10:00
    • Data Preprocessing 00:10:00
    • Importing Neural Network APIs 00:10:00
    • Getting Input Shape and Class Weights 00:10:00
    • Neural Network Model Building 00:10:00
    • Model Summary Explanation 00:10:00
    • Model Training 00:10:00
    • Model Evaluation 00:10:00
    • Model Saving and Loading 00:10:00
    • Prediction on Real-Life Data 00:10:00
    • Introduction to NLP 00:10:00
    • Key NLP Techniques 00:10:00
    • Overview of NLP Tools 00:10:00
    • Common Challenges in NLP 00:10:00
    • Bag of Words – The Simplest Word Embedding Technique 00:10:00
    • Term Frequency – Inverse Document Frequency (TF-IDF) 00:10:00
    • Loading the Spam Dataset 00:10:00
    • Text Preprocessing 00:10:00
    • Feature Engineering 00:10:00
    • Pair Plot 00:10:00
    • Train-Test Split 00:10:00
    • TF-IDF Vectorization 00:10:00
    • Model Evaluation and Prediction 00:10:00
    • Model Saving and Loading 00:10:00
    • Exam of Python Machine Learning Bootcamp 2023: From Data to Deep Learning 00:50:00
    • Premium Certificate 00:15:00
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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

1 day, 23 hours

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

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