Data Science and Machine Learning Mastery Course

4.6 ★★★★ ★

Data Science and Machine Learning Mastery Course

Name: Data Science and Machine Learning Mastery Course
Rating: 4.6 (308 reviews)
Author: AISciences

Data Science Python-Learn Statistics for Data Science, Machine Learning for Data Science, Deep Learning for Data Science

updated on icon Updated on Jun, 2024

language icon Language - English

person icon AISciences

English [CC]

category icon Python,Data Science and AI ML,Machine Learning

Lectures -725

Resources -1

Duration -94 hours

4.6 ★★★★ ★

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Course Description

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later.

Fast forward to today. It’s the same story with Artificial Intelligence (AI). The field of AI was formally founded in 1956. But it’s only now—more than six decades later—that AI is expected to revolutionize the way humanity will live and work in the coming decades.

Data science is a large field of study that covers data systems and processes. These systems and processes are aimed at maintaining data sets as well as getting meaning out of them. Machine Learning (ML), a branch of AI, is the concept that systems can automatically learn and adapt from experience without human intervention. ML, essentially, aims to equip machines with independent learning techniques.

Goals

1. A detailed understanding of Python for Data Science and Data Analysis.
2. Learn techniques to explore and understand data, as well as effectively visualize it using Python libraries like Pandas, Matplotlib and Seaborn.
3. Develop a strong foundation in probability theory and statistical methods,analyze and interpret data.
4. Hands on 3D interactive Plotting
5. Work on Geographic Maps with Folium using COVID-19 Data
6. Expertise in core concepts of machine learning, including supervised and unsupervised learning, as well as evaluation techniques.
7. Learn how to preprocess and engineer features to improve the performance of machine learning models.
8. Learn the AI fundamentals of artificial neural networks, gain insights into their architecture, training process, and applications.
9. Work on Face Verification: Problem Setup
10. Learn ANN, CNN, RNN.
11. Project 1- Book Writer
12. Project 2- Stock Price Prediction
13. Learn basics of reinforcement learning, understanding concepts such as agents, environments, rewards, and policies.
14. Project Frozenlake (Open AI Gym)

Data Science and Machine Learning Mastery Course

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction to the Course
3 Lectures

Introduction to the Course and Your Instructor 21:33 21:33
Introduction to Instructor 02:19 02:19
Course Code Notebooks and Materials

Basics for Data Science: Python for Data Science and Data Analysis
89 Lectures

Basics for Data Science: Data Understanding and Data Visualization with Python
97 Lectures

Basics for Data Science: Mastering Probability and Statistics in Python
124 Lectures

Machine Learning: Machine Learning Crash Course
51 Lectures

Machine Learning: Feature Engineering and Dimensionality Reduction with Python
67 Lectures

Deep learning: Artificial Neural Networks with Python
119 Lectures

Deep learning: Convolutional Neural Networks with Python
81 Lectures

Deep learning: Recurrent Neural Networks with Python
94 Lectures

Instructor Details

AISciences

Course Certificate

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