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Python Fundamentals:
Basics of Python syntax, variables, and data types.
String manipulation and formatting.
Operators and control flow statements.
Looping constructs.
Lists, tuples, sets, and dictionaries.
Functions and file handling.
Modules and packages.
Object-Oriented Programming (OOP) concepts including classes, objects, inheritance, and magic methods.
Introduction to Git and GitHub for version control.
SQL Fundamentals:
Introduction to SQL Server and its installation.
Understanding database normalization.
Writing SQL queries including joins and unions.
Data Analysis with Python:
Introduction to NumPy arrays and their manipulation.
Exploring Pandas Series and DataFrames for data analysis.
Handling missing data and performing data manipulation tasks.
Concatenating, merging, joining, and grouping data.
Data visualization using Matplotlib and Pandas built-in capabilities.
Machine Learning Fundamentals:
Understanding fundamental machine learning terminology.
Preprocessing data for machine learning tasks.
Model selection, evaluation, training, and validation.
Dealing with overfitting and underfitting.
Feature scaling techniques and encoding methods.
Regression and classification algorithms including linear regression, logistic regression, and decision trees.
Support vector machines (SVMs) and clustering algorithms like K-means.
Overview of ensemble techniques and deep learning concepts.
Advanced Topics:
Exploring advanced topics such as ensemble learning techniques and deep learning concepts.
Understanding the practical uses of Natural Language Processing (NLP) and exploring neural networks and language models.
Career Path Discussion:
Tips and guidelines for crafting an effective CV.
Strategies and resources for finding job opportunities in the field of data science and machine learning.
Projects:
Several projects throughout the course covering various machine learning algorithms, data analysis tasks, and data visualization projects.