Python for Machine Learning

Foundations of Machine Learning:

• Understanding the core principles and types of machine learning (supervised learning, unsupervised learning, and reinforcement learning).
• Exploring real-world applications of machine learning in various industries.

Python Programming Fundamentals:

• Brushing up on essential Python programming skills for machine learning.
• Covering data types, control structures, functions, and basic libraries (NumPy, Pandas) commonly used in machine learning.

Data Preprocessing and Exploration:

• Learning the importance of data quality and preprocessing.
• Exploring techniques for handling missing data, scaling features, and encoding categorical variables.
• Utilizing data visualization tools (Matplotlib, Seaborn) for exploratory data analysis.

Supervised Learning Algorithms:

• Comprehensive coverage of popular supervised learning algorithms such as linear regression, decision trees, support vector machines, and k-nearest neighbors.
• Hands-on implementation and evaluation of these algorithms using scikit-learn.

Unsupervised Learning Techniques:

• Introduction to unsupervised learning with clustering algorithms like K-means and hierarchical clustering.
• Dimensionality reduction techniques including Principal Component Analysis (PCA).

Model Evaluation and Hyperparameter Tuning:

• Understanding the metrics used to evaluate machine learning models.
• Exploring techniques for fine-tuning model parameters to enhance performance.

Introduction to Neural Networks and Deep Learning:

• Overview of neural networks and their role in deep learning.
• Building and training simple neural networks using TensorFlow or PyTorch.

Practical Project Work:

• Applying acquired knowledge to real-world projects.
• Collaborative problem-solving and code reviews.