Data Science With Python

Analysis, Visualisation & Machine Learning

What you'll learn

Become a Certified Data Scientist

Add Data Engineer to your CV

Master Python with a crash course

Implement Machine Learning Algorithms

Perform Classification and Regression

Grasp practical Natural Language Processing skills with Python

Master Data Science and the Machine Learning workflow

Gain an understanding on the correct model to choose for a given problem

Explore, visualise, pre-process and interpret large datasets

Perform statistical analysis on datasets

Work on an entire Data Science and Machine Learning project in Python and add it to your Portfolio

Requirements

There are no requirements for this course.

Students possessing a basic understanding of any programming language will find it easier to follow the course. But it is not a requirement.

Description

Are you interested in learning data science and machine learning with Python? If so, this course is for you! Designed for students and professionals who want to acquire practical knowledge and skills in data science and machine learning using Python, this course  covers various topics that are essential for building a strong foundation in data analysis, visualisation, and machine learning. The course covers various essential topics such as an overview of data science and machine learning concepts and terminology. Students will follow a crash course on Python Programming for a strong foundation for Data Science. They will learn about data analysis using Numpy and pandas, and data visualization using Matplotlib and seaborn. Students will also learn about data preprocessing, cleaning, encoding, scaling, and splitting for machine learning. The course covers a range of machine learning techniques, including supervised, unsupervised, and reinforcement learning, and various models such as linear regression, logistics regression, naives bayes, k-nearest neighbours, decision trees and random forests, support vector machines, and k-means clustering. In addition, students will get hands-on training with scikit-learn to train, evaluate, tune, and validate models. They will also learn about natural language processing techniques, including pre-processing, sentence segmentation, tokenization, POS tagging, stop word removal, lemmatization, and frequency analysis, and visualizing dependencies in NLP data. The final week of the course involves working on a final project and taking certification exams.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to Data Science

Section 2: Python Crash Course

Lecture 3 Python Fundamentals

Lecture 4 Advanced Python concepts

Lecture 5 Advanced Python programming

Section 3: Data Analysis

Lecture 6 Data analysis

Section 4: Machine Learning

Lecture 7 Machine learning

Lecture 8 Hands on: Machine Learning

Section 5: Natural Language Processing

Lecture 9 Natural Language Processing

Section 6: Project

Section 7: Exam

Anyone can take this course as it includes a Python Programming crash course to build your fundamentals too.,Students seeking to gain practical knowledge and skills in data analysis, visualisation, and machine learning using Python,Students who want to possess a highly sought skillset that will open up new career opportunities. (Data scientist, Data engineer, Data analyst)