Certified Machine Learning Engineer - Bronze

1. Understand the fundamental concepts of Data Science and Machine Learning.
2. Master basic mathematics crucial for ML, including linear algebra, probability, and statistics.
3. Learn to handle and preprocess data efficiently, ensuring high-quality inputs for your models.
4. Perform Exploratory Data Analysis (EDA) to uncover insights and trends within your data.
5. Implement essential ML algorithms such as linear regression and K-means clustering.
6. Evaluate and validate ML models to ensure accuracy and reliability.
7. Gain hands-on experience with Python, Jupyter Notebook, and key libraries like NumPy, Pandas, and Scikit-Learn.
8. Explore ethical considerations and best practices in responsible AI.

Agenda:

Introduction to Data Science and Machine Learning

        Overview of Data Science and its Applications.

        Introduction to Machine Learning concepts.

Basic Mathematics for Machine Learning

        Key concepts in linear algebra, probability, and statistics relevant to ML.

Data Handling and Preprocessing

        Types of data, data collection, and data cleaning.

        Hands-on: Cleaning and preprocessing a sample dataset.

Exploratory Data Analysis (EDA)

        Descriptive statistics and data visualization techniques.

        Hands-on: Performing EDA on a sample dataset using Python (Pandas, Matplotlib).

Introduction to Machine Learning Algorithms

        Supervised learning (regression and classification) and unsupervised learning (clustering).

        Hands-on: Implementing simple linear regression and K-means clustering.

Model Evaluation and Validation

        Train-test split, cross-validation, and performance metrics.

        Hands-on: Evaluating the performance of ML models using a sample dataset.

Practical Implementation

        Guided mini-project: Building a simple ML model to predict house prices.

            Data preprocessing.

            Model training and evaluation.

            Model tuning and optimization.

Ethics and Responsible AI

        Discussion on ethical considerations in AI and ML.

        Addressing bias, fairness, and transparency in ML models.