Overview

Embark on a comprehensive learning journey starting with fundamental Python programming, including installation, variable manipulation, and essential data structures like lists, tuples, and dictionaries. Gain proficiency in numerical computations with NumPy and data manipulation with Pandas. Strengthen your mathematical foundation with key linear algebra concepts vital for machine learning algorithms. Progress to data visualization using Matplotlib and Seaborn, interpreting and presenting data effectively. Develop a strong base in simple linear regression and gradient descent, and explore classification techniques with KNN and logistic regression through hands-on case studies. Dive into advanced machine learning algorithms, including regularization techniques and deep learning foundations, tailored for NLP applications. By course end, you'll have a robust understanding of implementing and optimizing machine learning models for NLP tasks, preparing you for advanced projects and career opportunities. Ideal for aspiring data scientists, machine learning enthusiasts, and professionals specializing in NLP, with basic Python and high school-level math knowledge required.

Syllabus

Prerequisite - Python Fundamentals

In this module, we will introduce the foundational aspects of Python, including installation and basic programming concepts. You will learn about variables, operations, loops, functions, and data structures such as strings, lists, tuples, sets, and dictionaries, preparing you for more advanced Python programming tasks.

Prerequisite - NumPy

In this module, we will cover the essential concepts of NumPy, focusing on array operations. You will learn how to perform various computations and manipulations with NumPy arrays, enabling efficient data handling in Python.

Prerequisite - Pandas

In this module, we will dive into Pandas, a powerful data manipulation library. You will learn about Series and DataFrames, data operations, indexing, merging, and pivot tables, equipping you with the skills to handle complex data analysis tasks.

Prerequisite - Some Fun with Math

In this module, we will explore linear algebra concepts crucial for machine learning. You will learn about vectors and matrices, perform various operations, and understand how to extend these concepts to higher dimensions, forming a solid mathematical foundation for advanced topics.

Prerequisite - Data Visualization

In this module, we will focus on data visualization techniques using Matplotlib and Seaborn. You will learn how to create and interpret visualizations, work on a case study, and apply these techniques to time series data, enhancing your ability to present and analyze data visually.

Prerequisite - Simple Linear Regression

In this module, we will introduce you to machine learning and linear regression. You will learn about the principles and mathematics behind linear regression, as well as how to apply it to real-world data through case studies, preparing you for more complex machine learning algorithms.

Prerequisite - Gradient Descent

In this module, we will cover gradient descent, a fundamental optimization technique. You will learn about its prerequisites, cost functions, optimization methods, and the differences between closed-form solutions and gradient descent, providing a strong basis for learning advanced machine learning algorithms.

Prerequisite - Classification: KNN

In this module, we will introduce classification and K-Nearest Neighbors (KNN). You will learn about classification principles, how to measure KNN's accuracy and effectiveness, and how to apply KNN to various problems, with practical case studies to reinforce your understanding.

Prerequisite - Logistic Regression

In this module, we will delve into logistic regression, an essential classification technique. You will learn about the Sigmoid function, log odds, and how to apply logistic regression to a case study, providing a robust understanding of this powerful tool.

Prerequisite - Advanced Machine Learning Algorithms

In this module, we will explore advanced machine learning algorithms and concepts. You will learn about regularization techniques, model selection, and performance evaluation through practical case studies, enhancing your ability to implement and optimize advanced models.

Prerequisite - Deep Learning introduction

In this module, we will introduce deep learning, covering its history, key concepts, and neural network structures. You will learn about training neural networks, activation functions, and representations, providing a comprehensive introduction to this transformative field in machine learning.