Machine Learning :: iNeuron

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Learn which Machine Learning model to choose for each type of problem and how to improve your Machine Learning Models. Become a Machine Learning Engineer and get hired.

3.88 (16 Reviews)

Language: English

Price

Free

Course Features

Lifetime Dashboard
Free Course
Certificate
Assignment
Quiz

Course Overview

Complete end to end hands-on machine learning tutorial with NumPy, Pandas, Seaborn , Matplotlib , Scikit-Learn , Flask , MongoDB etc from Machine Learning Expert. Learn best practices when it comes to Data Science Workflow by doing assignments, quizes and 5+ real time industry projects.

What you'll learn

Master Machine Learning on Python
Make robust Machine Learning models
Use Machine Learning for personal purpose
Handle advanced techniques like Dimensionality Reduction
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Design and evaluate A/B tests using T-Tests and P-Values
Data Visualization with MatPlotLib and Seaborn
Clean your input data to remove outliers

Requirements

Computer with Internet connectivity
Basic Programming understanding

Course Curriculum

Complete Road Map To Be Expert In Python- Follow My Way

Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials

Tutorial 1- Anaconda Installation and Python Basics

Why Python is the Best Programming Language For Machine Learning?

Tutorial 2 - Python List and Boolean Variables

Tutorial 3- Python Sets, Dictionaries and Tuples

Tutorial 4 - Numpy and Inbuilt Functions Tutorial

Tutorial 5- Pandas, Data Frame and Data Series Part-1

Tutorial 6- Pandas,Reading CSV files With Various Parameters- Part 2

Tutorial 7- Pandas-Reading JSON,Reading HTML, Read PICKLE, Read EXCEL Files- Part 3

Tutorial 8- Matplotlib (Simple Visualization Library)

Tutorial 9- Seaborn Tutorial- Distplot, Joinplot, Pairplot Part 1

Tutorial 10- Seaborn- Countplot(), Violinplot(), Boxplot()- Part2

How To Become Expertise in Exploratory Data Analysis

Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset

Tutorial 12- Python Functions, Positional and Keywords Arguments

Tutorial 13- Python Lambda Functions

Tutorial 15- Map Functions using Python

Tutorial 16- Filter Functions In Python

Tutorial 17- Python List Comprehension

Tutorial 18- Python Advanced String Formatting

Tutorial 19- Python Iterables vs Iterators

Tutorial 20- How To Import All Important Python Data Science Libraries Using Pyforest

Tutorial 21- Python OOPS Tutorial- Classes, Variables, Methods and Objects

Advanced Python- Exception Handling Detailed Explanation In Python

Advanced Python Series- Custom Exception Handling In Python

Advance Python Series- Public Private And Protected Access Modifiers

Tutorial 22-Univariate, Bivariate and Multivariate Analysis- Part1 (EDA)-Data Science

Tutorial 23-Univariate, Bivariate and Multivariate Analysis- Part2 (EDA)-Data Science

Tutorial 24- Histogram in EDA- Data Science

Tutorial 24-Z Score Statistics Data Science

Tutorial 25- Probability Density function and CDF- EDA-Data Science

Tutorial 26- Linear Regression Indepth Maths Intuition- Data Science

Tutorial 27- Ridge and Lasso Regression Indepth Intuition- Data Science

Tutorial 28- Ridge and Lasso Regression using Python and Sklearn

Multiple Linear Regression using python and sklearn

Tutorial 28-MultiCollinearity In Linear Regression- Part 2

Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting

Tutorial 29-R square and Adjusted R square Clearly Explained| Machine Learning

Tutorial 31- Hypothesis Test, Type 1 Error, Type 2 Error

Tutorial 32- All About P Value,T test,Chi Square Test, Anova Test and When to Use What?

Tutorial 33- P Value,T test, Correlation Implementation with Python- Hypothesis Testing

Tutorial 33- Chi Square Test Implementation with Python- Hypothesis Testing- Part 2

Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1

Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science

Tutorial 36- Logistic Regression Indepth Intuition- Part 2| Data Science

Tutorial 36- Logistic Regression Mutliclass Classification(OneVsRest)- Part 3| Data Science

Tutorial 37: Entropy In Decision Tree Intuition

Tutorial 38- Decision Tree Information Gain

Tutorial 39- Gini Impurity Intuition In Depth In Decision Tree

Tutorial 40- Decision Tree Split For Numerical Feature

Advance House Price Prediction- Exploratory Data Analysis- Part 1

Advance House Price Prediction- Exploratory Data Analysis- Part 2

Advance House Price Prediction-Feature Engineering Part 1

Advance House Price Prediction-Feature Engineering Part 2

Advance House Price Prediction-Feature Selection

Tutorial 41-Performance Metrics(ROC,AUC Curve) For Classification Problem In Machine Learning Part 2

Performance Metrics On MultiClass Classification Problems

K Nearest Neighbor classification with Intuition and practical solution

K Nearest Neighbour Easily Explained with Implementation

Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?

Tutorial 43-Random Forest Classifier and Regressor

Tutorial 45-Handling imbalanced Dataset using python- Part 1

Tutorial 46-Handling imbalanced Dataset using python- Part 2

Hyperparameter Optimization for Xgboost

What is AdaBoost (BOOSTING TECHNIQUES)

Visibility Climate Prediction- You Can Add This In Your Resume

Euclidean Distance and Manhattan Distance

K Means Clustering Intuition

Hierarchical Clustering intuition

DBSCAN Clustering Easily Explained with Implementation

Silhouette (clustering)- Validating Clustering Models- Unsupervised Machine Learning

Curse of Dimensionality Easily explained| Machine Learning

Dimensional Reduction| Principal Component Analysis

Principle Component Analysis (PCA) using sklearn and python

What is Cross Validation and its types?

Tutorial 42-How To Find Optimal Threshold For Binary Classification - Data Science

Tutorial 47- Bayes' Theorem| Conditional Probability- Machine Learning

Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning

Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning

Support Vector Machine (SVM) Basic Intuition- Part 1| Machine Learning

Maths Intuition Behind Support Vector Machine Part 2 | Machine Learning Data Science

Gradient Boosting In Depth Intuition- Part 1 Machine Learning

Gradient Boosting Complete Maths Indepth Intuiton Explained| Machine Learning- Part2

Xgboost Classification Indepth Maths Intuition- Machine Learning Algorithms

Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms

Data Science In Medical-Live Tracking Of CO--VID Cases In India using Python

Perform EDA In Seconds With Visualization Using SweetViz Library

4 End To End Projects Till Deployment For Beginners In Data Science| All You Have To Do Is Learn

Deploy Machine Learning Models Using StreamLit Library- Data Science

Perform Exploratory Data Analysis In Minutes- Data Science| Machine Learning

Pandas Visual Analysis- Perform Exploratory Data Analysis In A Single Line Of Code

How To Read And Process Huge Datasets in Seconds Using Vaex Library| Data Science| Machine Learning

D-Tale The Best Library To Perform Exploratory Data Analysis Using Single Line Of Code

Interview Prep Day3-How To Prepare Support Vector Machines Important Questions In Interviews

Google Datasets Search Engine- Search All Datasets From One Place For Data Science,Machine Learning

How To Run Flask In Google Colab

Time Series Forecasting Using Facebook FbProphet

Performance Metrics Interview Questions- Data Science

How To Perform Post Pruning In Decision Tree? Prevent Overfitting- Data Science

How To Train Machine Learning Model Using CPU Multi Cores

Step By Step Process To Learn Machine Learning Algorithm Efficiently

Data Science Is Just Not About Model Building

How To Interpret The ML Model? Is Your Model Black Box? Lime Library

6 Healthcare End To End Machine Learning Projects- Credits Devansh and Bedanta

Overfitting, Underfitting And Data Leakage Explanation With Simple Example

What Is API? Application Programming Interface And Why It Is Important-Data Science

500+ Machine Learning And Deep Learning Projects All At One Place

Google Colab Pro Vs Colab Free- Benefits Of Using Colab Pro- How To Access From India

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Krish Naik

Having 10+ years of experience in Data Science and Analytics with product architecture design and delivery. Worked in various product and service based Company. Having an experience of 5+ years in educating people and helping them to make a career transition.