Beginner Lessons

Tutorial 3- Deployment of NLP Model in Heroku Cloud

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

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

Tutorial 8- Matplotlib (Simple Visualization Library)

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

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

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

Tutorial 4 - Numpy and Inbuilt Functions Tutorial

Tutorial 3- Python Sets, Dictionaries and Tuples

Tutorial 2 - Python List and Boolean Variables

Tutorial 1- Anaconda Installation and Python Basics

Statistics- What is Pearson Correlation Coefficient? Difference between Correlation and Covariance

STATISTICS- What is Central Limit Theorem?

Guidelines To Prepare Resume For Data Scientist Role

STATISTICS- Mean, Median And Mode Explained Easily

What is AdaBoost (BOOSTING TECHNIQUES)

Tutorial 1- Deployment of Models- Premises VS IAAS vs PAAS cloud Platforms

Tutorial 43-Random Forest Classifier and Regressor

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

How To Learn Data Science Smartly?

Tutorial 22- Padding in Convolutional Neural Network

Tutorial 21- What is Convolution operation in CNN?

Tutorial 20- Convolution Neural Network vs Human Brain

Tutorial 37: Entropy In Decision Tree Intuition

What is Cross Validation and its types?

Tutorial 19- Training Artificial Neural Network using Google Colab GPU

Tutorial 18- Hyper parameter Tuning To Decide Number of Hidden Layers in Neural Network

Tutorial 17- Create Artificial Neural Network using Weight Initialization Tricks

Tutorial 5- Content Based Recommendation System

Tutorial 4- Book Recommendation using Collaborative Filtering

Tutorial 16- AdaDelta and RMSprop optimizer

Tutorial 3- Book Recommendation System using Pearson Correlation

Tutorial 2- Creating Recommendation Systems using Nearest Neighbors

Tutorial 15- Adagrad Optimizers in Neural Network

Tutorial 14- Stochastic Gradient Descent with Momentum

Tutorial 13- Global Minima and Local Minima in Depth Understanding

Tutorial 12- Stochastic Gradient Descent vs Gradient Descent

Tutorial 11- Various Weight Initialization Techniques in Neural Network

Tutorial 10- Activation Functions Rectified Linear Unit(relu) and Leaky Relu Part 2

Tutorial 9- Drop Out Layers in Multi Neural Network

Tutorial 7- Vanishing Gradient Problem

Tutorial 3-Activation Functions Part-1

Tutorial 2- How does Neural Network Work

Tutorial 1- Introduction to Neural Network and Deep Learning

Tutorial 46-Handling imbalanced Dataset using python- Part 2

Tutorial 45-Handling imbalanced Dataset using python- Part 1

Random Forest(Bootstrap Aggregation) Easily Explained

K Nearest Neighbour Easily Explained with Implementation