Machine Learning With Python

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