Free Download Machine Learning With Python 2024Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.71 GB | Duration: 8h 1m
Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!
What you'll learn
learn how to use data science and machine learning with Python.
Understand Machine Learning from top to bottom.
Learn NumPy for numerical processing with Python.
Create supervised machine learning algorithms to predict classes.
Requirements
No prior knowledge of machine learning required. Basic knowledge of Python
Description
Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence.
Overview
Section 1: Machine Learning With Python 2023
Lecture 1 Introduction to Course
Lecture 2 What is Machine Learning
Lecture 3 Life Cycle
Lecture 4 Introduction to Numpy Library
Lecture 5 Creating Arrays from Scratch
Lecture 6 Creating Arrays from Scratch Continued
Lecture 7 Array Indexing and Slicing
Lecture 8 Numpy Array Functions and Shape Modification
Lecture 9 Mathematical Operations on Numpy Arrays
Lecture 10 Introduction to Pandas Library
Lecture 11 Working with Pandas DataFrames
Lecture 12 Slicing and Indexing with Pandas
Lecture 13 Create DataFrame and Explore Dataset
Lecture 14 Data Analysis with Pandas DataFrame
Lecture 15 Other Useful Methods in Pandas Library
Lecture 16 Introduction to Matplotlib
Lecture 17 Customizing Line Plots
Lecture 18 Create Plot Using DataFrame
Lecture 19 Standard Scaler to Scale the Data
Lecture 20 Encoding Categorical Data
Lecture 21 Sklearn Pipeline and Column Transformer
Lecture 22 Evaluation Metrics in Sklearn
Lecture 23 Linear Regression
Lecture 24 Evaluation of Linear Regression Model
Lecture 25 Polynomial Regression
Lecture 26 Polynomial Regression Continued
Lecture 27 Sklearn Pipeline Polynomial Regression
Lecture 28 Decision Tree Classifier
Lecture 29 Decision Tree Evaluation
Lecture 30 Random Forest
Lecture 31 Support Vector Machines
Lecture 32 Kmeans Clustering
Lecture 33 KMeans Clustering - Hands On
Lecture 34 Data Loading and Analysis
Lecture 35 Dimensionality Reduction with PCA
Lecture 36 Hyper Parameter Tuning
Lecture 37 Summary
Section 2: Machine Learning with Python Case Study - Covid19 Mask Detector
Lecture 38 Introduction to Course
Lecture 39 Getting System Ready
Lecture 40 Read and Write Images
Lecture 41 Resize and Crop
Lecture 42 Working with Shapes
Lecture 43 Working with Text
Lecture 44 Pre-Requisite for Face Detection
Lecture 45 Detect the Face
Lecture 46 Introduction to Deep Learning with Tensorflow
Lecture 47 Model Building
Lecture 48 Training the Mask Detector
Lecture 49 Saving the Best Model
Lecture 50 Basic Front End Design of App
Lecture 51 File Upload Interface for App
Lecture 52 App Prep
Lecture 53 App Build and Testing
Lecture 54 AWS Deployment
Lecture 55 AWS Deployment Continued
Section 3: Machine Learning Python Case Study - Diabetes Prediction
Lecture 56 Introduction to Pima Indians Diabetes Using Machine Learning
Lecture 57 Installation of Anaconda
Lecture 58 Installation of Libraries
Lecture 59 Steps in Machine Learning
Lecture 60 Dataset and Logistic Regression
Lecture 61 Pima Classification
Lecture 62 Exclude the Header
Lecture 63 Conversion of String into Number
Lecture 64 Split the Dataset
Lecture 65 Check the ROC
Anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
Homepage
https://www.udemy.com/course/machine-learning-with-python-e/
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