← Machine Learning & Deep Learning

Fees by level

Start at any level, or take the complete programme. The fee you pay for a level is locked for you.

All fees are in Indian Rupees and include applicable taxes. See our pricing & payment terms.

The complete curriculum

01 Introduction 3 lessons ▶

1. 1.1Intro to ML and its types
2. 1.2Applications and Challenges
3. 1.3Intro to Python Module

02 Python Programming 12 lessons ▶

1. 2.1Python Data Types
2. 2.2Data Types Practical
3. 2.3List in Python
4. 2.4Tuples in Python
5. 2.5Dictionary in Python
6. 2.6Sets in Python
7. 2.7Loops in Python
8. 2.8Comprehension in Python
9. 2.9Functions in Python
10. 2.10Lambda Functions in Python
11. 2.11Exception Handling in Python
12. 2.12Regular Expressions in Python

03 Data Preprocessing in Python 11 lessons ▶

1. 3.1Dataset Preparation
2. 3.2Training and Testing Set
3. 3.3Importing Dataset
4. 3.4Essential Libraries for Pre Processing
5. 3.5Numpy Introduction
6. 3.6Numpy Operations & Manipulation
7. 3.7Numpy Bonus Operations
8. 3.8Pandas in Python
9. 3.9Pandas Operations for Data
10. 3.10Pandas Bonus Operations
11. 3.11Matplotlib in Python

04 Handling Missing Data & Feature Scaling 2 lessons ▶

1. 4.1Handling Missing Data
2. 4.2Feature Scaling in ML

05 Data Preprocessing in R 3 lessons ▶

1. 5.1What is R
2. 5.2R Installation
3. 5.3Preprocessing Codes in R

06 Linear Regression 4 lessons ▶

1. 6.1Linear Regression
2. 6.2Linear Regression Code
3. 6.3Plot for Predicted vs. Test Data
4. 6.4Linear Regression using R

07 Multiple Linear Regression 3 lessons ▶

1. 7.1Multiple Linear Regression
2. 7.2Multiple Linear Regression Code
3. 7.3Multiple Linear Regression using R

08 Polynomial Regression 3 lessons ▶

1. 8.1Polynomial Regression
2. 8.2Polynomial Regression Code
3. 8.3Polynomial Regression using R

09 Support Vector Regression 3 lessons ▶

1. 9.1Support Vector Regression
2. 9.2SVR Code
3. 9.3SVR using R

10 Decision Tree Regression 3 lessons ▶

1. 10.1Decision Tree Regression
2. 10.2Decision Tree Code
3. 10.3Decision Tree using R

11 Random Forest Regression 3 lessons ▶

1. 11.1Random Forest Regression
2. 11.2Random Forest Regression Code
3. 11.3Random Forest in R

12 Evaluation of Regression Models 3 lessons ▶

1. 12.1Evaluation of Regression Models
2. 12.2Regression Model Selection in Python
3. 12.3Evaluation Metrics Bonus

13 Logistic Regression 2 lessons ▶

1. 13.1Logistic Regression & Code
2. 13.2Logistic Regression using R

14 Support Vector Machine 2 lessons ▶

1. 14.1Support Vector Machine and Code
2. 14.2SVM using R

15 K Nearest Neighbors (KNN) 3 lessons ▶

1. 15.1K Nearest Neighbours
2. 15.2KNN Code
3. 15.3KNN using R

16 Decision Tree Classification 2 lessons ▶

1. 16.1Decision Tree Classification and Code
2. 16.2Decision Tree using R

17 Random Forest Classification 2 lessons ▶

1. 17.1Random Forest Classification and Code
2. 17.2Random Forest Using R

18 Naive Bayes Classification 2 lessons ▶

1. 18.1Naive Bayes Classification & Code
2. 18.2Naive Bayes using R

19 Evaluation of Classification Models 1 lesson ▶

1. 19.1Evaluation Metrics in Classification

20 K Means Clustering 4 lessons ▶

1. 20.1K Means Clustering
2. 20.2K Means Clustering Code Python
3. 20.3Optimising K Means Clustering and Elbow Method
4. 20.4K Means Clustering using R

21 Hierarchical Clustering 3 lessons ▶

1. 21.1Hierarchical Clustering
2. 21.2Hierarchical Clustering using Python Code
3. 21.3Hierarchical Clustering using R

22 Apriori Algorithm 4 lessons ▶

1. 22.1Apriori Algorithm
2. 22.2Market Basket Analysis
3. 22.3Apriori Implementation in Python
4. 22.4Apriori Code using R

23 ECLAT Algorithm 3 lessons ▶

1. 23.1ECLAT Algorithm
2. 23.2ECLAT Code
3. 23.3ECLAT vs Apriori

24 Reinforcement Learning 7 lessons ▶

1. 24.1What is Reinforcement Learning
2. 24.2Multi-Armed Bandit Exploration vs Exploitation
3. 24.3Upper Confidence Bound Algorithm
4. 24.4UCB using Python and R
5. 24.5Thompson Sampling Algorithm
6. 24.6Deterministic vs Probabilistic UCB & Thompson Sampling
7. 24.7Thompson Sampling Code

25 Natural Language Processing 17 lessons ▶

1. 25.1NLP-Introduction, Challenges & Applications
2. 25.2Introduction to NLTK
3. 25.3Stop Word Removal
4. 25.4Tokenization
5. 25.5Stemming and Lemmatization
6. 25.6Named Entity Recognition
7. 25.7POS Tagging
8. 25.8POS Tagging Approach & Applications
9. 25.9Rare Word Removal
10. 25.10Correcting Words using NLTK
11. 25.11Bag of Words Technique
12. 25.12Word Embeddings
13. 25.13What is Sentiment Analysis
14. 25.14Sentiment Analysis Implementation
15. 25.15Sentiment Analysis with machine Learning
16. 25.16ML for Sentiment Analysis Code
17. 25.17Training & Hyperparameter Tuning for Sentiment Analysis

26 ML Model Deployment 3 lessons ▶

1. 26.1What is Model Deployment
2. 26.2Introduction to streamlit
3. 26.3Model Deployment using Streamlit

27 Deep Learning : Artificial Neural Networks 8 lessons ▶

1. 27.1Introduction to Deep Learning: History and Context
2. 27.2Machine Learning vs. Deep Learning
3. 27.3DL Basics Neurons,Synapses,Activation Funcns
4. 27.4Understanding Activation Functions
5. 27.5Artificial Neural Networks
6. 27.6Gradient Descent vs Brute Force Optimization
7. 27.7ANN Code in Python
8. 27.8ANN using R code

28 Deep Learning : Convolutional Neural Networks 7 lessons ▶

1. 28.1Convolutional Neural Networks
2. 28.2Understanding Layers of CNN
3. 28.3Convolution in CNN
4. 28.4Keras & TensorFlow for CNN
5. 28.5CNN using Python
6. 28.6Introduction to Transformers
7. 28.7Encoder Decoder Achitecture

29 Dimensionality Reduction 6 lessons ▶

1. 29.1What is Dimensionality Reduction
2. 29.2Principal Component Analysis
3. 29.3PCA in Python
4. 29.4PCA using R
5. 29.5Linear Discriminant Analysis and Code
6. 29.6Kernel PCA and Code

30 Model Selection and Boosting 2 lessons ▶

1. 30.1K Fold Cross Validation & Code
2. 30.2Gradient Boosting for Classification Problems

31 Capstone Project 1 lesson ▶

1. 31.1ML Project

Tools you'll use

Related courses