Machine Learning with Python Online Course (10 credits)


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Sale price€199.50

Machine learning is a subset of artificial intelligence (AI) and a fascinating field of computer science. It gives computers the ability to perform tasks without being explicitly programmed. Machine Learning by generating and training data models automates the process of data analysis and creates data-informed predictions in real-time without the need for human interaction. In the data science lifecycle, this is where machine learning algorithms are applied. Data science has been utilizing machine learning since machines can discover patterns in data, learn the patterns, and generate predictions. Machine learning has found its way into many of our daily services, like Google Search, YouTube recommender, and iPhone virtual assistant Siri.

TechClass Machine Learning with Python online course dives into practical machine learning using the well-known programming language, Python. It provides a unique opportunity for you to get hands-on experience with popular Python libraries for machine learning such as Numpy, Matplotlib, Pandas, Seaborn, and Scikit-learn. By the end of this course, you will be able to implement your machine learning models (supervised and unsupervised) from scratch, get them to work, and evaluate their performance. Furthermore, common practices and tricks used by data scientists and machine learning experts are also described throughout the course to prepare you for future job opportunities.

Learning outcomes

  • Learn how to set up and get started with Jupyter Notebook for Python
  • Learn how to work with Numpy arrays and how to use different Numpy functions
  • Learn how to use Matplotlib to produce basic plots of data and results
  • Learn how to use Pandas library to work with tabular data to manipulate them
  • Get familiar with the Scikit-learn library and how it can be used to implement machine learning algorithms
  • Get familiar with data preprocessing operations and how to apply them using Pandas and Scikit-learn libraries
  • Gain hands-on experience with implementing, training, and evaluating linear and non-linear regression models
  • Gain hands-on experience with implementing, training, and evaluating logistic regression, SVM, K-NN, and decision tree classifiers
  • Learn how to tune the hyper-parameters of the learning algorithms to achieve better performances
  • Learn how to implement the PCA algorithm for dimensionality reduction.
  • Learn how to implement the k-means algorithm to perform clustering on unlabeled data
  • Gain hands-on experience with data visualization and performance visualization with different types of plots
  • Learn how to extract different metrics to facilitate model evaluation and selection

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: An Introduction to Machine Learning

  • 2.1. Machine Learning
  • 2.2. Machine Learning Applications
  • 2.3. Why Python?
  • 2.4. Exercise

Chapter 3: Getting Started with Python

  • 3.1. Jupyter Notebook
  • 3.2. Setting up Jupyter Notebook
  • 3.3. Getting Started with Jupyter Notebook
  • 3.4. Python Basics: Syntax and Variables
  • 3.5. Python Basics: Operators
  • 3.6. Python Basics: Data Types
  • 3.7. Python Basics: Decision Making
  • 3.8. Python Basics: Loops
  • 3.9. Python Basics: Defining Functions
  • 3.10. Python Libraries for Machine Learning
  • 3.11. Quiz

Chapter 4: NumPy

  • 4.1. Introduction
  • 4.2. Arrays
  • 4.3. Array Math
  • 4.4. Array Indexing
  • 4.5. Exercise

Chapter 5: Matplotlib

  • 5.1. Introduction
  • 5.2. Plot
  • 5.3. Subplot and Scatter Plot
  • 5.4. OOI and Easy Subplotting
  • 5.5. Exercise

Chapter 6: Pandas

  • 6.1. Introduction
  • 6.2. Loading Data
  • 6.3. Accessing DataFrame Elements
  • 6.4. Basic Statistics and Missing Values
  • 6.5. Querying and GroupBy
  • 6.6. Exercise

Chapter 7: Regression

  • 7.1. Scikit-learn
  • 7.2. Linear Regression: Introduction
  • 7.3. Linear Regression: Implementation
  • 7.4. Exercise 1
  • 7.5. K-Nearest Neighbors Regression: Introduction
  • 7.6. K-Nearest Neighbors Regression: Implementation
  • 7.7. Exercise 2

Chapter 8: Classification

  • 8.1. Logistic Regression: Introduction
  • 8.2. Logistic Regression: Implementation
  • 8.3. Exercise 1
  • 8.4. Support Vector Machines: Introduction
  • 8.5. Support Vector Machines: Implementation
  • 8.6. Exercise 2
  • 8.7. K-Nearest Neighbors Classifier: Introduction
  • 8.8. K-Nearest Neighbors Classifier: Implementation
  • 8.9. Exercise 3
  • 8.10. Decision Tree: Introduction
  • 8.11. Decision Tree: Implementation
  • 8.12. Exercise 4

Chapter 9: Unsupervised Learning

  • 9.1. Principal Component Analysis: Introduction
  • 9.2. Principal Component Analysis: Implementation
  • 9.3. k-Means Clustering: Introduction
  • 9.4. k-Means Clustering: Implementation
  • 9.5. Exercise

Chapter 10: Final Tasks

  • 10.1. Project
  • 10.2. Self-study Essay
  • 10.3. Congrats! You did it!

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