Data Science for AI and Machine Learning Using Python

Data Science for AI and Machine Learning Using Python, available at $19.99, has an average rating of 4.2, with 64 lectures, based on 31 reviews, and has 211 subscribers.

You will learn about 1. The content (80% hands on and 20% theory) will prepare you to work independently on Data Science (AI and Machine learning) project 2. Foundation of Machine learning 3. Supervised Machine learning – Regression 4. Supervised Machine learning – Classifications 5. Unsupervised Machine learning (Clustering, KNN, PCA) 6. Text Analytics 7. Time Series This course is ideal for individuals who are 1. Want to work in AI/ML. 2. Already working in AI/ML. 3. Like to bring Insight from Data It is particularly useful for 1. Want to work in AI/ML. 2. Already working in AI/ML. 3. Like to bring Insight from Data.

Enroll now: Data Science for AI and Machine Learning Using Python

Summary

Title: Data Science for AI and Machine Learning Using Python

Price: $19.99

Average Rating: 4.2

Number of Lectures: 64

Number of Published Lectures: 64

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

1. The content (80% hands on and 20% theory) will prepare you to work independently on Data Science (AI and Machine learning) project
2. Foundation of Machine learning
3. Supervised Machine learning – Regression
4. Supervised Machine learning – Classifications
5. Unsupervised Machine learning (Clustering, KNN, PCA)
6. Text Analytics
7. Time Series

Who Should Attend

1. Want to work in AI/ML. 2. Already working in AI/ML. 3. Like to bring Insight from Data

Target Audiences

1. Want to work in AI/ML. 2. Already working in AI/ML. 3. Like to bring Insight from Data

Becoming Data Science professional (Data Scientist) is a long journey and need guidance from seasoned Data Science professional (Chief Data Scientist). We are trying to manage the journey such a way that you learn right skills and in the right way. The whole concepts of the course are to make you ready for Data Science projects, mainly in Machine learning and AI projects. You will learn

1. Foundation of Machine learning

2. Supervised Machine learning – Regression

3. Supervised Machine learning – Classifications

4. Unsupervised Machine learning (Clustering, KNN, PCA)

5. Text Analytics

6. Time Series

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Data Science – Brief Introduction

Lecture 1: Data Science – Brief Introduction

Chapter 3: Foundation – Panda

Lecture 1: Header

Lecture 2: Panda read csv

Lecture 3: datatype and statistics

Lecture 4: Panda column operations

Lecture 5: Panda operations

Lecture 6: Merge and concat

Lecture 7: Tables

Lecture 8: Graphs

Chapter 4: Foundation – Numpy

Lecture 1: One Dimension

Lecture 2: Two Dimension

Lecture 3: Two Dimension stacking

Chapter 5: Foundation – Descriptive Analysis

Lecture 1: Data Dictionary

Lecture 2: Single numeric descriptive analysis

Lecture 3: Double numeric descriptive analysis

Lecture 4: Categorical and all Numeric Descriptive Analysis

Chapter 6: Regression

Lecture 1: Introduction and Preprocessing

Lecture 2: Feature Selection Regularisation

Lecture 3: Residual Analysis

Lecture 4: Data Read

Lecture 5: Normality test and BoxCox transformation

Lecture 6: Linear Regression structure

Lecture 7: Linear Regression for Numeric features

Lecture 8: HotEncoding and Scaling

Lecture 9: Linear Regression with HotEncoding and Scaling Data

Lecture 10: Generic Treeflow in Prediction

Lecture 11: CatBoost

Lecture 12: CatBoost Hyperparameter Tuning

Lecture 13: XGBoost

Lecture 14: XGBoost setup DMatrix

Lecture 15: XGBoost Modelling

Chapter 7: Classification

Lecture 1: Classification Introduction

Lecture 2: Classification: Code and data load

Lecture 3: Classification: Random Forest

Lecture 4: Classification: Random Forest code

Lecture 5: Classification: CatBoost code

Lecture 6: Classification: One class SVM code

Lecture 7: Classification:Logistic Regression

Lecture 8: Classification: Logistic Regression code

Chapter 8: How to know models are good enough using Bias vs Variance

Lecture 1: How to know models are good enough Bias vs Variance

Chapter 9: Clustering

Lecture 1: Clustering: Introduction

Lecture 2: Clustering: KMeans

Lecture 3: Clustering: Agglomerative

Lecture 4: Clustering: KNN

Lecture 5: Clustering:KNN using Iris

Chapter 10: Application of Unsupervised and Supervised Analytics

Lecture 1: Application of Unsupervised and Supervised Analytics

Chapter 11: Text Analytics

Lecture 1: Text Analytics: Introduction

Lecture 2: Text Analytics: NLTK Installation

Lecture 3: Text Analytics: Tokenization TextBlob

Lecture 4: Named-entity recognition (NER)-Stemming-Lemmatization

Lecture 5: Word Cloud

Chapter 12: Time Series

Lecture 1: Time Series: Introduction

Lecture 2: Time Series: Basic Statistics

Lecture 3: Time Series: Dickey-Fuller and Decomposition

Lecture 4: Time Series: Missing value imputation

Lecture 5: Time Series: Anomaly detection

Lecture 6: Time Series: Anomaly detection code

Lecture 7: Time Series: ARIMA_Forecasting

Lecture 8: Time Series: ARIMA Forecasting code

Lecture 9: Time Series: SARIMAX Forecasting Tunning

Lecture 10: Time Series: SARIMAX Forecasting code

Lecture 11: Time Series: Prophet

Lecture 12: Time Series: Prophet code

Instructors

Shiv Onkar Deepak Kumar
AI Researcher and Consultant, Chief Data Scientist

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 9 votes
4 stars: 6 votes
5 stars: 14 votes

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