Machine Learning with Python -90 hours

A comprehensive two-part course, designed for both beginners and seasoned programmers.

Part 1: Introduction to Machine Learning with Python (45 hours)

Delve into the fundamentals of machine learning with Python, covering essential concepts such as data preprocessing, model training, and evaluation. Gain hands-on experience through practical exercises that build a solid foundation for understanding algorithms and their applications.

Part 2: Advanced Machine Learning with Python (45 hours)

Dive deeper into cutting-edge machine learning techniques, exploring topics like deep learning, reinforcement learning, and natural language processing. Collaborate with industry experts and tackle real-world challenges, honing your ability to develop sophisticated machine learning models using Python libraries and frameworks.

Detailed Curriculum

This syllabus covers a comprehensive range of topics, from the foundational libraries in Python for machine learning to advanced concepts such as neural networks and recommender systems. It provides a well-rounded understanding of the key aspects of machine learning and its practical applications.

1. Libraries for Data Handling, Preprocessing, and Visualization in Python:

• NumPy
• pandas
• matplotlib
• seaborn
• scikit-learn

2. Complete Process for Creating a Machine Learning Application:

• Overview of the entire process from data collection to model deployment.

3. Data Processing Before Machine Learning:

• Importance of data preprocessing before feeding it to a machine learning algorithm.
• Handling missing data.

4. Dimensionality Reduction, Feature Extraction & Selection:

• Techniques for reducing the number of features in a dataset.

5. Various Types of Machine Learning Algorithms and When to Use Each:

• Supervised Learning:

• Regression: Simple linear, polynomial, LASSO, etc.
• Classification: KNN, Decision Trees, Random Forests, Support Vector Machines.
• Ensemble learning.

• Unsupervised Learning:

• Clustering: K-Means Clustering, Hierarchical Clustering, Density-Based Clustering.
• Semi-supervised learning.
• Reinforcement learning.

6. Evaluation of Machine Learning Models:

• Techniques such as train-test-split, cross-validation, r2 score, accuracy, handling overfitting vs. underfitting, confusion matrix.

7. Recommender Systems:

• Introduction to recommender systems.
• Collaborative Filtering and Content-Based Recommendation.

8. Neural Networks and Deep Learning:

• Introduction to neural networks.
• Overview of deep learning.

Who can attend

The course "Machine Learning with Python" is designed for a diverse audience with varying levels of programming experience and interest in machine learning.

1. Beginner Programmers:

• Individuals with basic programming knowledge or newcomers to Python.
• Those interested in understanding the fundamentals of machine learning and its application using Python.

2. Intermediate Programmers:

• Programmers familiar with Python looking to deepen their understanding of machine learning concepts.
• Individuals who want to gain hands-on experience with building and evaluating machine learning models.

3. Data Scientists and Analysts:

• Professionals working with data who want to expand their skill set to include machine learning using Python.
• Those seeking practical knowledge in implementing machine learning algorithms on real-world datasets.

4. Software Developers:

• Developers interested in incorporating machine learning techniques into their applications using Python.
• Individuals looking to explore advanced machine learning concepts to enhance their software development skills.

5. Business and IT Professionals:

• Decision-makers and professionals from non-technical backgrounds who want a comprehensive overview of machine learning with Python.
• Those aiming to understand the potential applications and impact of machine learning in their respective industries.

6. Anyone Interested in Advanced Machine Learning:

• Individuals with a solid foundation in machine learning who want to explore advanced topics and stay updated with the latest advancements.
• Those interested in specialized areas such as deep learning, reinforcement learning, and natural language processing.

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