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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