Machine Learning & Deep Learning in 30hrs

Udemy

Acquire the knowledge and skills to develop data science and machine learning projects using Python, Keras, and Pandas.

Online

₹ 799

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Course Introduction - 6 videos

AI Hype
Mario's Triangle & The Skills Gap
Why is this course differnt?
Software & Hardware Needs
Course Details - AI Subjects Covered
Hands On Introduction

Definitions, Fundamentals and History of AI - 5 videos

What is AI, what is Machine Learning and what is Deep Learning?
History of AI
Regression Problem & Cost Function
Classification
Supervised, Unsupervised and Reinforcement Learning

Linear Algebra and Calculus Refresher - 11 Videos

Scalars and Vectors 1
Scalars and Vectors 2
Matrices 1
Matrices 2
Eigenvalues and Eigenvectors
Tensors
Derivatives
Convex Functions
Partial Derivatives
Lagrange Multipliers
Taylor Series

Gradient Descent and Optimizers - 10 videos

Gradient Descent 1
Gradient Descent 2
Multi-Variate Gradient Descent
Contour Plots
Newton's Method
Stochastic Gradient Descent
Optimizers | Adagrad | RMSprop
Optimizers | Momentum | Adam
Feature Scaling | Feature Normalisation
Gradient Descent Summary

Classification and Classification Performance Indicators - 4 videos

Classification - Introduction
Confusion Matrix
ROC Curve | AUC
F1 Score

K nearest neighbours (KNN) - 8 Videos

Introduction to KNN
How to select K?
Curse of Dimentionality
Coding Brief
Coding 1/4
Coding 2/4
Coding 3/4
Coding 4/4

Dimensionality Reduction - 7 Videos

Introduction
Principal Component Analisys (PCA) 1
Principal Component Analisys (PCA) 2
Principal Component Analisys (PCA) 3
Singular Value Decomposition (SVD) 1
Singular Value Decomposition (SVD) 2
Coding

Statistics Refresher - 13 Videos

Introduction
Random Variable & Probability Distribution
Bayes' Rule
MLE and MAP
Expected Value and Variance of a Random Variable
Bernoulli Distribution
Binomial Distribution 1
Binomial Distribution 2
Gaussian Distribution 1
Gaussian Distribution 2
Weak Law of Large Numbers (WLLN)
Central Limit Theorem (CLT)
Covariance and Variance Matrix

The Perceptron - 6 videos

The Perceptron 1
The Perceptron 2
XOR Problem
Activation Function
SoftMax and ReLu
Summary

Naive Bayes - 10 Videos

Introduction
Naive Bayes 1
Naive Bayes 2
Naive Bayes - Continuous Case
Naive Bayes - Discreet Case 1
Naive Bayes - Discreet Case 2
Coding Brief
Coding 1/3
Coding 2/3
Coding 3/3

Logistic Regression - 14 Videos

Introduction
Logistic Regression - 1
Normal Equation
Logistic Regression - 2
Logistic Regression - 3
Logistic Regression - 4
Logistic Regression - 5
Logistic Regression - 6
Logistic Regression - 7
Coding Brief
Coding 1/3
Coding 2/3
Coding 3/3
Coding Results

Support Vector Machines - 12 Videos

Introduction
SVM 1
SVM 2
SVM 3
SVM 4
Kernel Trick
Coding Brief
Coding Brief - Hyperparameter Tuning
Coding 1/4
Coding 2/4
Coding 3/4
Coding 4/4 + Results

Underfitting, Overfitting & Regularizaion - 5 Videos

Introduction
Bias Variance Decomposition
Regularization 1
Regularization 2
Regularization 3

Decision Trees - 28 Videos

Classifiers: Taking Stock
KD Trees 1
KD Trees 2
Information Content
Entropy
Cross-Entropy 1
Cross-Entropy 2
KL Divergence
Entropy over a Tree
Bagging
Random Forest
Random Forest: Coding
Boosting
ADABoost 1
ADABoost 2
Gradient Boosting 1
Gradient Boosting 2
Gradient Boosting 3
Gradient Boosting 4
Gradient Boosting 5
Gradient Boosting vs. Gradient Descent
Coding Brief 1
Coding Brief 2: Hyperparameter Tuning: GridSearch & Random Search
Coding Brief 3: Hyperparameter Tuning: Bayesian Optimization
Coding Brief 4
Coding 1/3
Coding 2/3
Coding 3/3

Unsupervised Learning - Recommender Systems - 8 Videos

Taking Stock
Introduction to Recommender Systems
Content Based Systems
Collaborative Filtering 1
Collaborative Filtering 2
Collaborative Filtering 3
Collaborative Filtering 4
Collaborative Filtering 5

Unsupervised Learning - Anomaly Detection

Unsupervised Learning & Anomaly Detection Introduction
Anomaly Detection Systems: Simple Statistics & Density Based Methods
Anomaly Detection Systems: Local Outlier Factor (LOF)
Anomaly Detection Systems: One Class SVM
Anomaly Detection Systems: Isolated Forest
Anomaly Detection Systems: Coding Brief
Coding 1/4
Coding 2/4
Coding 3/4
Coding 4/4
Coding Results

Unsupervised Learning - Clustering

Introduction Clustering
K-Means 1
K-Means 2
Coding Brief
Coding 1/2
Coding 2/2

Deep Learning - 15 Videos

Introduction
The Perceptron
From Perceptron to Neural Networks
Neural Networks Terminology
Coding Brief
Coding 1/1
Backpropagation 1
Backpropagation 2
Coding Brief
Coding 1/1
Regularization
Coding 1/3
Coding 2/3
Coding 3/3
Results Brief

Convolutional Neural Networks - Computer Vision: 34 Videos

Covnets Introduction 1
Covnets Introduction 2
Analog & Digital Signals
Signal Theory - LTI Systems 1
Signal Theory - LTI Systems 2
Signal Theory - LTI Systems 3
Signal Theory - LTI Systems 4
Why do we need Covnets?
Covnet Elements
Covnet Architecture 1
Covnet Architecture 2
Coding Brief Covnets
Coding - Data Upload
Coding Brief Data Preparation 1
Coding Brief Data Preparation 2
Coding 1/2
Coding 2/2
Data Augmentation
Coding 1/1
Feature Extraction
ResNet 1
ResNet 2
ResNet 3
ResNet 4
Transfer Learning - Feature Extraction
Coding: Transfer Learning - ResNet50 1/3
Coding: Transfer Learning - ResNet50 2/3
Coding: Transfer Learning - ResNet50 3/3
Coding: Transfer Learning - VGG16
Transfer Learning - Results
Transfer Learning - Finetuning
Coding VGG16 - Finetuning
Coding ResNet - Finetuning
Coding Results Finetuning

Dealing with Text Data - 13 Videos

Introduction
From Words to Vectors
T-SNE
Word Embeddings CBOW 1
Word Embeddings CBOW 2
Word Embeddings CBOW 3
Word Embeddings SKIP GRAM
Negative Sampling 1
Negative Sampling 2
Hierarchical Softmax
Direct co-occurence count
Glove
Batch Normalization

Recurrent Neural Networks - 11 Videos

Introduction
BPTT 1
BPTT 2
BPTT 3
Selective Write, Read and Forget
LSTM & GRU
Coding Brief
Coding Simple RNN
Coding Transfer Learning - GloVe
Coding LSTM
Coding Results

RNNs & Time Series - 13 Videos

Coding Brief 1
Coding Brief 2
Coding 1/3
Coding 2/3
Coding 3/3
Recurrent Dropout & Stacking
Coding Recurrent Dropout and Stacking
Bidirectional RNNs
Coding Bidirectional RNNs
1D Covnet
Coding 1D Covnet
Coding 1D Covnet + Recurrent Dropout
Coding Results
Sequence to Sequence Models
Attention
Transformers

Generative Deep Learning: Everybody an Artist

Introduction
Coding Brief
Coding 1/3
Coding 2/3
Coding 3/3
Neural Style Transfer 1
Neural Style Transfer 2
Variational Autoencoders (VAEs) 1
Variational Autoencoders (VAEs) 2
Variational Autoencoders (VAEs) 3
Variational Autoencoders (VAEs) 4
Variational Autoencoders (VAEs) 5
Variational Autoencoders (VAEs) 6
Variational Autoencoders (VAEs) 7
GANs 1
GANs 2
GANs 3
GANs 4
GANs 5
GANs 6

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Machine Learning & Deep Learning in 30hrs

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