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Data Science With Python udemy (2023) - SKIKDA - 05-25-2023
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Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.74 GB | Duration: 11h 21m
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)
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