Machine Learning Programming with Python

Course description:

In this course you will program in Python using the most popular Data Science tools and libraries and learn how to use it for advanced statistical analysis, data pre-processing and transformation, developing data visualizations, and developing predictive models to solve a variety of problems.

The course has hands-on exercises for each module (with direct applicability to banking), so not only will you learn the theory, but you’ll also tackle the practical side of building your own models, which you can then use as starting points in building your own projects.

You will also learn how to improve models developed by machine learning algorithms, by delving into the concepts of underfitting, overfitting, cross-validation and how hyper-parameters could improve the performance of models (hyper-parameter tuning).

Prerequisites:

  • Basic programming knowledge in Python.
  • Fundamentals of linear algebra and differential calculus.
  • An understanding of the fundamental concepts of statistics and probability.

Who is it for:

The Machine Learning with Python course is designed for people who want to learn about machine learning techniques and algorithms and how to apply them in practical projects using Python.

What will you learn:

  • Define Artificial Intelligence and Machine Learning, understanding their significance and applications in various domains.
  • Identify the different types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, and comprehend their specific use cases.
  • Comprehend the model life cycle and the essential steps involved in building a machine learning model, from data collection to deployment.
  • Perform Exploratory Data Analysis (EDA) to gain insights into the dataset, identify patterns, and understand the relationships between variables.
  • Learn data preprocessing techniques to clean and prepare the data for machine learning algorithms, including handling missing values and treating outliers.
  • Explore various machine learning algorithms used for classification, regression, clustering, and more, understanding their strengths and weaknesses.
  • Implement a practical example of Supervised Learning for loan classification to predict defaults, gaining hands-on experience in building a predictive model.
  • Master Feature Engineering techniques, including handling missing values, treating outliers, addressing rare categories, and processing categorical variables for machine learning.
  • Understand the significance of Feature Selection to enhance model performance and reduce complexity, exploring Filter Methods, Embedded Methods, and Wrapper Methods.
  • Gain insights into the importance of reducing dimensional space for modeling and identify appropriate techniques for achieving dimensionality reduction.
  • Apply Filter Methods (e.g., basis elements, correlation, statistical measures) for feature selection, understanding how they impact model performance.
  • Implement Embedded Methods (e.g., Lasso, Random Forest) to automatically perform feature selection during the model training process.
  • Explore Agnostic Methods like Recursive Feature Elimination (RFE) to iteratively select the most important features for modeling.
  • Develop practical skills in Python programming for machine learning, using popular libraries like NumPy, Pandas, Scikit-learn, and more
  • What is Artificial Intelligence and Machine Learning
  • Types of Machine Learning
  • Model life cycle: Steps in building a model
  • Exploratory data analysis
  • Data preprocessing
  • Machine Learning algorithms
  • Practical example of Supervised Learning: Loan classification to predict default
  • Feature Engineering
    – Techniques for handling missing values
    – Treatment of outliers
    – Treatment of rare categories
    – Multiple techniques for the treatment of categorical variables
    – Data processing and transformation techniques required for the main groups of machine learning algorithms
  • Feature Selection (Filter Methods, Embedded Methods, Wrapper Methods)
    – Importance of dimensional space reduction for modelling
    – Application of filter methods (basis elements, correlation, statistical measures)
    – Application of embedded methods (lasso, random forrest, etc.)
    – Application of agnostic methods (RFE)”

Certification:

Certificate of completion.

Machine Learning Programming with Python

€ 840
  • DURATION: 2 days
  • SKILL LEVEL: Associate
  • LECTURES: 9 lessons

Register
  • PRICE: 840 €
  • DURATION: 2 days
  • SKILL LEVEL: Associate
  • LECTURES: 9 lessons

Course description:

In this course you will program in Python using the most popular Data Science tools and libraries and learn how to use it for advanced statistical analysis, data pre-processing and transformation, developing data visualizations, and developing predictive models to solve a variety of problems.

The course has hands-on exercises for each module (with direct applicability to banking), so not only will you learn the theory, but you’ll also tackle the practical side of building your own models, which you can then use as starting points in building your own projects.

You will also learn how to improve models developed by machine learning algorithms, by delving into the concepts of underfitting, overfitting, cross-validation and how hyper-parameters could improve the performance of models (hyper-parameter tuning).

Prerequisites:

  • Basic programming knowledge in Python.
  • Fundamentals of linear algebra and differential calculus.
  • An understanding of the fundamental concepts of statistics and probability.

Who is it for:

The Machine Learning with Python course is designed for people who want to learn about machine learning techniques and algorithms and how to apply them in practical projects using Python.

What will you learn:

  • Define Artificial Intelligence and Machine Learning, understanding their significance and applications in various domains.
  • Identify the different types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, and comprehend their specific use cases.
  • Comprehend the model life cycle and the essential steps involved in building a machine learning model, from data collection to deployment.
  • Perform Exploratory Data Analysis (EDA) to gain insights into the dataset, identify patterns, and understand the relationships between variables.
  • Learn data preprocessing techniques to clean and prepare the data for machine learning algorithms, including handling missing values and treating outliers.
  • Explore various machine learning algorithms used for classification, regression, clustering, and more, understanding their strengths and weaknesses.
  • Implement a practical example of Supervised Learning for loan classification to predict defaults, gaining hands-on experience in building a predictive model.
  • Master Feature Engineering techniques, including handling missing values, treating outliers, addressing rare categories, and processing categorical variables for machine learning.
  • Understand the significance of Feature Selection to enhance model performance and reduce complexity, exploring Filter Methods, Embedded Methods, and Wrapper Methods.
  • Gain insights into the importance of reducing dimensional space for modeling and identify appropriate techniques for achieving dimensionality reduction.
  • Apply Filter Methods (e.g., basis elements, correlation, statistical measures) for feature selection, understanding how they impact model performance.
  • Implement Embedded Methods (e.g., Lasso, Random Forest) to automatically perform feature selection during the model training process.
  • Explore Agnostic Methods like Recursive Feature Elimination (RFE) to iteratively select the most important features for modeling.
  • Develop practical skills in Python programming for machine learning, using popular libraries like NumPy, Pandas, Scikit-learn, and more
  • What is Artificial Intelligence and Machine Learning
  • Types of Machine Learning
  • Model life cycle: Steps in building a model
  • Exploratory data analysis
  • Data preprocessing
  • Machine Learning algorithms
  • Practical example of Supervised Learning: Loan classification to predict default
  • Feature Engineering
    – Techniques for handling missing values
    – Treatment of outliers
    – Treatment of rare categories
    – Multiple techniques for the treatment of categorical variables
    – Data processing and transformation techniques required for the main groups of machine learning algorithms
  • Feature Selection (Filter Methods, Embedded Methods, Wrapper Methods)
    – Importance of dimensional space reduction for modelling
    – Application of filter methods (basis elements, correlation, statistical measures)
    – Application of embedded methods (lasso, random forrest, etc.)
    – Application of agnostic methods (RFE)”

Certification:

Certificate of completion.

Register

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