← Machine Learning & Deep Learning

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The complete curriculum

01 Introduction to Course 1 lesson ▶

1. 1.1Course Intro DL

02 Introduction to Deep Learning 2 lessons ▶

1. 2.1Introduction to Deep Learning_ History and Context
2. 2.2Machine learning vs deep learning

03 Artificial Neural Networks 6 lessons ▶

1. 3.1DL Basics Neurons,Synapses,Activation Funcns
2. 3.2Understanding Activation Functions
3. 3.3Artificial Neural Networks
4. 3.4Gradient Descent vs Brute Force Optimization
5. 3.5ANN Code in Python
6. 3.6ANN using R code

04 Convolutional Neural Network 7 lessons ▶

1. 4.1Convolutional Neural Networks
2. 4.2Understanding Layers of CNN
3. 4.3Convolution in CNN
4. 4.4Keras & TensorFlow for CNN
5. 4.5CNN using Python
6. 4.6Introduction to Transformers
7. 4.7Encoder Decoder Achitecture

05 Recurrent Neural Network 5 lessons ▶

1. 5.1What is RNN & Working of RNN
2. 5.2Vanishing Gradient Problem in RNN
3. 5.3LSTM in Deep Learning
4. 5.4Working of LSTM
5. 5.5Variations of LSTM

06 Building RNN 1 lesson ▶

1. 6.1Building RNN - CODE

07 Self Organizing Maps 7 lessons ▶

1. 7.1What is Self Organizing Maps(SOM)
2. 7.2Creation & Working of SOM
3. 7.3K Means Clustering Introduction
4. 7.4K Means Clustering
5. 7.5K Means Clustering Code Python
6. 7.6Optimising K Means Clustering and Elbow Method
7. 7.7K Means Clustering using R

08 Building Self Organizing Maps 1 lesson ▶

1. 8.1Building SOM - CODE

09 Boltzmann Machines 3 lessons ▶

1. 9.1Boltzmann Machines
2. 9.2Boltzmann Machines vs Neural Networks
3. 9.3Boltzmann Machine Code in Python

10 Auto Encoders 4 lessons ▶

1. 10.1Auto Encoders in Deep Learning
2. 10.2Types of Autoencoders
3. 10.3Autoencoders in Machine Learning
4. 10.4Training Process in Autoencoders

11 Building an Autoencoder 1 lesson ▶

1. 11.1AutoEncoder Code in Python

12 Annexure : Machine Learning Basics 6 lessons ▶

1. 12.1Linear Regression
2. 12.2Logistic Regression
3. 12.3Support Vector Machine
4. 12.4K Nearest Neighbors Algorithm
5. 12.5Decision Trees
6. 12.6Random Forest

13 Data preprocessing in Python 13 lessons ▶

1. 13.1Dataset Preparation
2. 13.2Training and Testing Set
3. 13.3Importing Dataset
4. 13.4Essential Libraries for Pre Processing
5. 13.5Numpy Introduction
6. 13.6Numpy Operations & Manipulation
7. 13.7Numpy Bonus Operations
8. 13.8Pandas in Python
9. 13.9Pandas Operations for Data
10. 13.10Pandas Bonus Operations
11. 13.11Matplotlib in Python
12. 13.12Handling Missing Data
13. 13.13Feature Scaling in ML

14 Sentiment Analysis for Deep Learning 3 lessons ▶

1. 14.1Sentiment Analysis using Deep Learning
2. 14.2Data Preparation for Sentiment Analysis using Deep Learning
3. 14.3Train Test Split for Sentiment Analysis Using Deep Learning

15 Poetry Generation in Deep Learning 1 lesson ▶

1. 15.1Poetry Generation in NLP

16 Deep Learning for Advanced AI 4 lessons ▶

1. 16.1Deep Learning for Advanced AI RL & Gen AI
2. 16.2What is Reinforcement Learning
3. 16.3Introduction to GEN AI
4. 16.4Prompt Engineering - Techniques & Examples

17 Capstone Project 1 lesson ▶

1. 17.1DL Capstone Project

Tools you'll use

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