2.1: What is Pandas python? Introduction and Installation
2.2: Dataframe Basics
2.3: Different Ways Of Creating DataFrame
2.4: Read Write Excel CSV File
2.5: Handle Missing Data: fillna, dropna, interpolate
2.6: Handle Missing Data: replace function
2.7: Group By (Split Apply Combine)
2.8: Concat Dataframes

3.1: Introduction and Installation
3.2: format strings in plot function
3.3: Axes labels, Legend, Grid
3.4: Bar Chart
3.5: Histograms
3.6: Pie Chart

4.1: How to become a data scientist for free? | Step by step approach to become data scientist
4.2: What is Machine Learning?
4.3: Linear Regression Single Variable
4.4: Linear Regression Single Variable Quiz
4.5: Linear Regression Single Variable Exercise
4.6: Linear Regression Multiple Variables
4.7: Linear Regression with Multiple Variables Quiz
4.8: Linear Regression Multiple Variables Exercise
4.9: Gradient Descent and Cost Function
4.10: Gradient Descent and Cost Function Quiz
4.11: Gradient Descent and Cost Function Exercise
4.12: Save Model Using Joblib And Pickle
4.13: Dummy Variables & One Hot Encoding
4.14: Dummy Variables & One Hot Encoding Quiz
4.15: Dummy Variables & One Hot Encoding Exercise
4.16: Training and Testing Data
4.17: Logistic Regression (Binary Classification)
4.18: Logistic Regression (Binary Classification) Quiz
4.19: Logistic Regression (Binary Classification) Exercise
4.20: Logistic Regression (Multiclass Classification)
4.21: Logistic Regression (Multiclass Classification) Exercise
4.22: Decision Tree
4.23: Decision Tree Exercise
4.24: Support Vector Machine (SVM)
4.25: Support Vector Machine (SVM) Exercise
4.26: Support Vector Machine Quiz
4.27: Random Forest
4.28: Random Forest Exercise
4.29: Random Forest Quiz
4.30: K Fold Cross Validation
4.31: K Fold Cross Validation Exercise
4.32: K Fold Cross Validation Quiz
4.33: K Means Clustering
4.34: K Means Clustering Exercise
4.35: K Means Clustering Quiz
4.36: Naive Bayes Part 1
4.37: Naive Bayes Part 2
4.38: Naive Bayes Quiz
4.39: Hyper parameter Tuning (GridSearchCV)
4.40: Hyper parameter Tuning (GridSearchCV) Quiz
4.41: Hyper parameter Tuning (GridSearchCV) Exercise
4.42: L1 and L2 Regularization | Lasso, Ridge Regression
4.43: L1 and L2 Regularization | Lasso, Ridge Regression Quiz

5.1: Introduction
5.2: Data Cleaning
5.3: Feature Engineering
5.4: Outlier Removal
5.5: Model Building
5.6: Python Flask Server
5.7: Website or UI
5.8: Deploy machine learning model to production AWS
5.9: Data Analyst vs Data Engineer vs Data Scientist

6.1: Introduction
6.2: Data Collection
6.3: Data Cleaning
6.4: Feature Engineering
6.5: Training a Model
6.6: Flask Server
6.7: Build Website
6.8: Deployment & Exercise

7.1: Introduction
7.2: Why deep learning is becoming so popular?
7.3: What is a neuron?
7.4: What is a Neural Network
7.5: Install tensorflow 2.0
7.6: Pytorch vs Tensorflow vs Keras
7.7: Neural Network For Handwritten Digits Classification
7.8: Activation Functions
7.9: Derivatives
7.10: Derivatives Exercise
7.11: Matrix Basics
7.12: Matrix Basics Exercise
7.13: Loss or Cost Function
7.14: Loss or Cost Function Exercise
7.15: Gradient Descent For Neural Network
7.16: Implement Neural Network In Python
7.17: Stochastic Gradient Descent vs Batch Gradient Descent vs Mini Batch Gradient Descent
7.18: Stochastic Gradient Descent vs Batch Gradient Descent vs Mini Batch Gradient Descent Exercise
7.19: Chain Rule
7.20: Tensorboard Introduction
7.21: GPU bench-marking with image classification
7.22: GPU bench-marking with image classification Exercise
7.23: Customer churn prediction using ANN
7.24: Customer churn prediction using ANN Exercise
7.25: Precision, Recall, F1 score, True Positive
7.26: Handling imbalanced dataset in machine learning
7.27: Handling imbalanced dataset in machine learning Exercise
7.28: Applications of computer vision
7.29: Simple explanation of convolutional neural network
7.30: Image classification using CNN (CIFAR10 dataset)
7.31: Image classification using CNN (CIFAR10 dataset) Exercise
7.32: Convolution padding and stride
7.33: Data augmentation to address overfitting
7.34: Transfer Learning
7.35: Image classification vs Object detection vs Image Segmentation
7.36: Popular datasets for computer vision: ImageNet, Coco and Google Open images
7.37: Sliding Window Object Detection
7.38: What is YOLO algorithm?
7.39: Object detection using YOLO v4 and pre trained model
7.40: What is Recurrent Neural Network (RNN)?
7.41: Types of RNN | Recurrent Neural Network Types
7.42: Vanishing and exploding gradients
7.43: Simple Explanation of LSTM
7.44: Simple Explanation of GRU (Gated Recurrent Units)
7.45: Bidirectional RNN
7.46: Converting words to numbers, Word Embeddings
7.47: Word embedding using keras embedding layer
7.48: What is Word2Vec?
7.49: Implement word2vec in gensim
7.50: Distributed Training On NVIDIA DGX Station A100
7.51: Tensorflow Input Pipeline
7.52: Tensorflow Input Pipeline Exercise
7.53: Optimize Tensorflow Pipeline Performance: prefetch & cache

What is a Neural Network

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Contents

1: Data Science Full Course For Beginners

1.1: What is Data Science?

1.2: Learn data science for beginners

1.3: Install python on windows

1.4: Variables in python

1.5: Variables in python Exercise

1.6: Numbers

1.7: Numbers Exercise

1.8: Strings

1.9: Strings Exercise

1.10: Lists

1.11: List Exercise

1.12: Install PyCharm on Windows

1.13: Debug Python code using PyCharm

1.14: If Statement

1.15: For loop

1.16: For Loop Exercise

1.17: Functions

1.18: Functions Exercise

1.19: Dictionaries and Tuples

1.20: Dictionaries and Tuples Exercise

1.21: Install Python Module (using pip)

1.22: Modules

1.23: Working With JSON

1.24: if __name__ == "__main__"

1.25: Exception Handling

1.26: Class and Objects

1.27: What is Jupyter Notebook?

1.28: What is Anaconda? Install Anaconda On Windows.

1.29: Jupyter Notebook Tutorial / Ipython Notebook Tutorial

1.30: numpy tutorial - basic array operations

2: Python Pandas Tutorial

2.1: What is Pandas python? Introduction and Installation

2.2: Dataframe Basics

2.3: Different Ways Of Creating DataFrame

2.4: Read Write Excel CSV File

2.5: Handle Missing Data: fillna, dropna, interpolate

2.6: Handle Missing Data: replace function

2.7: Group By (Split Apply Combine)

2.8: Concat Dataframes

3: Matplotlib Tutorial

3.1: Introduction and Installation

3.2: format strings in plot function

3.3: Axes labels, Legend, Grid

3.4: Bar Chart

3.5: Histograms

3.6: Pie Chart

4: Machine Learning Tutorial Python

4.1: How to become a data scientist for free? | Step by step approach to become data scientist

4.2: What is Machine Learning?

4.3: Linear Regression Single Variable

4.4: Linear Regression Single Variable Quiz

4.5: Linear Regression Single Variable Exercise

4.6: Linear Regression Multiple Variables

4.7: Linear Regression with Multiple Variables Quiz

4.8: Linear Regression Multiple Variables Exercise

4.9: Gradient Descent and Cost Function

4.10: Gradient Descent and Cost Function Quiz

4.11: Gradient Descent and Cost Function Exercise

4.12: Save Model Using Joblib And Pickle

4.13: Dummy Variables & One Hot Encoding

4.14: Dummy Variables & One Hot Encoding Quiz

4.15: Dummy Variables & One Hot Encoding Exercise

4.16: Training and Testing Data

4.17: Logistic Regression (Binary Classification)

4.18: Logistic Regression (Binary Classification) Quiz

4.19: Logistic Regression (Binary Classification) Exercise

4.20: Logistic Regression (Multiclass Classification)

4.21: Logistic Regression (Multiclass Classification) Exercise

4.22: Decision Tree

4.23: Decision Tree Exercise

4.24: Support Vector Machine (SVM)

4.25: Support Vector Machine (SVM) Exercise

4.26: Support Vector Machine Quiz

4.27: Random Forest

4.28: Random Forest Exercise

4.29: Random Forest Quiz

4.30: K Fold Cross Validation

4.31: K Fold Cross Validation Exercise

1.24: if name == "main"