Machine Learning

This comprehensive course covers the fundamentals of Machine Learning including supervised and unsupervised learning algorithms, model evaluation, feature engineering and more. Learn how to process and represent data, perform model evaluation, and feature engineering, and understand machine learning concepts.

Discuss the future and impact of Machine Learning on society, including ethics and safety concerns. Gain a strong understanding of Machine Learning and its applications by the end of the course.

Key Learning Outcomes: 

• 1. Define machine learning and its different types (supervised and unsupervised) and understand their applications.
• 2. Apply supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and k-nearest neighbours (k-NN).
• 3. Implement unsupervised learning techniques, such as K-means clustering.
• 4. Evaluate machine learning models and perform hyperparameter tuning to improve model performance.
• 5. Perform feature engineering and dimensionality reduction techniques, such as feature extraction, feature selection, feature scaling, and PCA.
• 6. Analyse the future and trends of machine learning, including its impact on society and the ethics and safety concerns associated with machine learning.
• 7. Synthesize the concepts and techniques of machine learning into a comprehensive understanding of the field.

What You will Learn?

• Fundamentals of Machine Learning
• Data Preprocessing and Visualization
• Supervised Learning Techniques
• Advanced Machine Learning Concepts

Curriculum:

• Machine Learning-Introduction
• ·         Introduction of Machine Learning
• ·         Evolution of Machine Learning
• ·         Application of Machine Learning
• Machine Learning in Different Sectors
• ·         Introduction
• ·         AI & ML for Products or Services
• ·         How Google Uses Artificial Intelligence and Data Science
• ·         How LinkedIn Uses Artificial Intelligence and Data Science
• ·         How Amazon Uses Artificial Intelligence and Data Science
• ·         Netflix: Using Artificial Intelligence and Data Science to Drive Engagement
• ·         Media and Entertainment Industry
• ·         Education Industry
• ·         Healthcare Industry
• ·         Government
• ·         Weather Forecasting
• ·         Key Takeaways
• Machine Learning-Fundamental
• ·         Difference between Traditional Programming and ML Programming
• ·         Requirements for Machine Learning Practical Implementation
• ·         Required software and tools for Machine Learning implementations.
• ·         Setup Anaconda
• ·         Installation of PyCharm or Spyder or Jupyter
• ·         Configure PyCharm/Spyder/Jupyter with Anaconda
• Python Programming Foundation for Data Science
• ·         Introduction
• ·         Variables
• ·         Data Types with Python
• ·         Assisted Practice: Data Types in Python
• ·         Keywords and Identifiers
• ·         Expressions
• ·         Basic Operators
• ·         Operators in Python
• ·         Functions
• ·         Search for a Specific Element from a Sorted List
• ·         String Operations
• ·         String Operations in Python
• ·         Tuples
• ·         Tuples in Python
• ·         Lists
• ·         Lists in Python
• ·         Sets
• ·         Sets in Python
• ·         Dictionaries
• ·         Dictionary in Python
• and more