Embark on an extensive 18-hour Machine Learning and Deep Learning Bootcamp Series offered by The AI University. Dive deep into the OSEMN framework for data science, covering crucial topics such as data extraction, cleaning, and analysis. Learn to work with various data sources including MongoDB, MySQL, and APIs. Master essential techniques like web scraping, data simulation, and handling missing values. Explore feature engineering concepts including one-hot encoding, feature scaling, and outlier detection. Gain proficiency in data transformation methods and pandas operations. Delve into exploratory data analysis, covering univariate and bivariate analysis techniques. Understand the differences between AI, ML, and Deep Learning. Acquire hands-on experience with regression techniques, including simple and multiple linear regression, polynomial regression, and decision trees. Explore classification algorithms such as logistic regression, support vector machines, k-nearest neighbors, and random forests. Learn to evaluate model performance using confusion matrices, precision-recall, CAP curves, and ROC curves. Conclude with insights on automating the data science lifecycle and machine learning pipeline.
Machine Learning and Deep Learning Bootcamp Series
Overview
Syllabus
Free Data Science Course Online - OSEMN Framework | Introduction.
Free Data Science Course Online - OSEMN Framework | Requirement Gathering in Data Science.
Data Science Awesome Framework (Data Extraction Primer) - Step 2 | Data Extraction in ML.
MongoDB in 25 minutes (Machine Learning) | Extract Data from NoSQL DB MongoDB for Machine Learning.
Hands-on with Web Scraping using Python and Beautifulsoup | Data Extraction for Machine Learning.
How to Extract Data using API | What is an API and How exactly it works | Python Code Part 1.
How to Extract Data using API | What is an API and How exactly it works | Python Code Part 2.
Load or Extract Data from MySQL DB or CSV file for ML | Extract Data using Sklearn Python for ML.
Simulate Machine Learning Classification Data | Data Simulation Technique using Python.
Simulate Machine Learning Regression & Clustering Data | Data Simulation Technique using Python.
Scrubbing or Cleaning Bad data in Data Science (Python) - Step 3 | Data Munging in Machine Learning.
3.Dataset Missing Values & Imputation (Detailed Python Tutorial) | Impute Missing values in ML.
4.One Hot Encoding to process Categorical variables (Python) | Process Categorical Features.
5.Split data into Training and Test set in Data Science (Python) | Train Test Split function in ML.
6.Feature Scaling in Machine Learning(Normalization & Standardization) | Feature Scaling Sklearn.
7.Outlier Detection and Treatment using Python - Part 1 | How to Detect outliers in Machine Learning.
8.Outlier Detection and Treatment using Python - Part 2 | How to Detect outliers in Machine Learning.
9.Outlier Detection and Treatment using Python - Part 3 | How to Detect outliers in Machine Learning.
Log Transformation for Outliers | Convert Skewed data to Normal Distribution.
Outlier Treatment through Square Root Transformation | Convert Skewed data to Normal Distribution.
Python Pandas Tutorial Series: Using Map, Apply and Applymap.
Use Regular Expression to split string into Dataframe columns (Pandas).
Python Pandas Tutorial - Adding & Dropping columns (Machine Learning).
Python Pandas Tutorial - Merge Dataframes (Machine Learning).
Python Exploratory Data Analysis (OSEMN Framework) - Step 4.
Univariate Analysis for Categorical Variables using Python.
Univariate Analysis for Numerical Variables (Exploratory Data Analysis) Intuition.
Convert Numerical variable to Categorical Intuition | BINNING.
BINNING | Convert Numerical variable to Categorical using Python.
How to do Bivariate Analysis of Numerical Numerical Variables.
Chi Square Test | How to do Bivariate Analysis of Categorical Categorical Variables.
z-test & t-test | Bivariate Analysis for Numerical-Categorical Variables.
Bivariate Analysis for Numerical-Categorical Variables|ANOVA|Data Science EDA.
Don't know the Difference among AI, ML and Deep Learning ?.
Simple Linear Regression using Scikit Learn & Spark MLLib | Introduction & Intuition.
Simple Linear Regression using Scikit Learn & Spark MLLib | Data Pre-processing | Code Part 1.
Simple Linear Regression using Scikit Learn & Spark MLLib | Building Model | Code Part 2.
Simple Linear Regression using Scikit Learn & Spark MLLib | Graphical Comparison | Code Part 3.
Simple Linear Regression | Scikit Learn & Spark MLLib | Model Evaluation Techniques - Part 1.
Simple Linear Regression | MSE RMSE & MAE | Model Evaluation Techniques - Part 2.
Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 1 | Install Spark on Jupyter Notebook.
Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 2 | Install Spark on Jupyter Notebook.
Run PySpark on Google Colab for FREE! | PySpark on Jupyter.
Simple Linear Regression using Spark MLLib | Introduction.
Simple Linear Regression using Spark MLLib | Data Preprocessing.
Simple Linear Regression using Spark MLLib | Build Train & Evaluate Model.
Multiple Linear Regression using Scikit Learn | Introduction & Intuition.
Multiple Linear Regression using Scikit Learn | Coding Part 1.
Multiple Linear Regression using Scikit Learn | Coding Part 2.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 1.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 2.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 3.
Polynomial Regression using Python | Polynomial Regression Machine Learning.
Polynomial Regression Machine Learning Python Code.
Decision Tree Regression Introduction and Intuition.
Complete End to End Python code for Decision Tree Regression.
Random Forest Regression Introduction and Intuition.
Complete End to End Python code for Random Forest Regression.
Fantastic Explanation of Logistic Regression in Machine Learning - Part 1.
Fantastic Explanation of Logistic Regression Model - Part 2.
How to Develop and Train Logistic Regression model on Titanic Dataset | Python Code Part 1.
How to Develop and Train Logistic Regression model on Titanic Dataset | Python Code Part 2.
Best Explanation of Confusion Matrix False Positive False Negative so far.
Precision Recall and F1-Score Explanation in Easy way.
How to use CAP curve for Classification Model Evaluation? | What is CAP Curve?.
Best Explanation of Evaluating Classification Model using AUC-ROC Curve.
How to Generate AUC-ROC curve for Evaluating Logistic Regression Model?.
Fantastic Explanation of Support Vector Machine algorithm | Support Vector Machine Intuition.
Build Train and Evaluate Support Vector Machine Model | Train & Evaluate SVM model using python.
Fantastic Explanation of K-Nearest Neighbor | K-Nearest Neighbor Introduction and Intuition.
Build Train & Evaluate K-Nearest Neighbor (KNN) Model | Using Python & Scikit Learn.
Decision Tree Classification Introduction and Intuition.
What are the 4 Key Steps to Create a Decision Tree? | How to Create Decision tree?.
How to Measure the Purity of Decision Tree split using GINI INDEX | How to calculate Gini Index?.
How to Measure the Purity of Decision Tree split using Information Gain.
Build, Train & Evaluation Decision Tree Model | Decision Tree using Scikit Learn.
How to Train Random Forest Classifier using Python? | How does Random Forest work?.
How to AUTOMATE Data Science Lifecycle | How to AUTOMATE Machine Learning Pipeline.
डेटा Science Lifecycle को स्वचालित कैसे करें | How to AUTOMATE Data Science Lifecycle in Hindi.
Taught by
The AI University
