Machine Learning Essentials (2023)

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Mohit Uniyal,Prateek Narang

27:57:01

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1. Introduction

1. Course Overview.mp4

03:34

2. Artificial Intelligence.mp4

02:54

3. Machine Learning.mp4

06:58

4. Deep Learning.mp4

03:54

5. Computer Vision.mp4

03:06

6. Natural Language Processing.mp4

04:20

7. Automatic Speech Recognition.mp4

06:43

8. Reinforcement Learning.mp4

02:49

9. Pre-requisites.html

10. Code Repository.html

2. Supervised vs Unsupervised Learning

1. Supervised Learning Introduction.mp4

05:22

2. Supervised Learning Example.mp4

13:08

3. Unsupervised Learning.mp4

09:13

3. Linear Regression

1. Introduction to Linear Regression.mp4

01:41

2. Notation.mp4

11:39

3. Hypothesis.mp4

06:12

4. Loss Error Function.mp4

12:45

5. Training Idea.mp4

03:24

6. Gradient Descent Optimisation.mp4

06:52

7. Gradient Descent Code.mp4

17:32

8. Gradient Descent - for Linear Regression.mp4

04:11

9. The Math of Training.mp4

08:13

10. Code 01 - Data Generation.mp4

13:41

11. Code 02 - Data Normalisation.mp4

11:15

12. Code 03 - Train Test Split.mp4

12:23

13. Code 04 - Modelling.mp4

21:43

14. Code 05 - Predictions.mp4

06:19

15. R2 Score.mp4

09:49

16. Code 06 - Evaluation.mp4

05:28

17. Code 07 - Visualisation.mp4

06:57

18. Code 08 - Trajectory [Optional].mp4

08:04

4. Linear Regression - Multiple Features

1. Introduction.mp4

06:13

2. Hypothesis.mp4

06:04

3. Loss Function.mp4

02:25

4. Training & Gradient Updates.mp4

06:44

5. Code 01 - Data Prep.mp4

16:10

6. Code 02 - Hypothesis.mp4

05:49

7. Code 03 - Loss Function.mp4

03:46

8. Code 04 - Gradient Computation.mp4

15:10

9. Code 05 - Training Loop.mp4

11:53

10. A Note about Shapes.mp4

02:21

11. Code 06 - Evaluation.mp4

03:46

12. Linear Regression using Sk-Learn.mp4

05:39

5. Logistic Regression

1. Binary Classification Introduction.mp4

05:26

2. Notation.mp4

06:44

3. Hypothesis Function.mp4

22:44

4. Binary Cross-Entropy Loss Function.mp4

13:27

5. Gradient Update Rule.mp4

15:45

6. Code 01 - Data Prep.mp4

11:21

7. Code 02 - Hypothesis Logit Model.mp4

06:53

8. Code 03 - Binary Cross Entropy Loss.mp4

03:33

9. Code 04 - Gradient Computation.mp4

07:15

10. Code 05 - Training Loop.mp4

10:20

11. Code 06 - Visualise Decision Boundary.mp4

06:40

12. Code 07 - Predictions & Accuracy.mp4

08:36

13. Logistic Regression using Sk-Learn.mp4

04:47

14. Multiclass Classification One Vs Rest.mp4

11:11

15. Multiclass Classification One Vs One.mp4

04:29

6. Dimensionality Reduction Feature Selection

1. Curse of Dimensionality.mp4

04:46

2. Feature Selection Vs. Feature Extraction.mp4

04:58

3. Filter Method.mp4

06:47

4. Wrapper Method.mp4

07:21

5. Embedded Method.mp4

03:10

6.1 train.csv

6. Feature Selection - Code.mp4

11:18

7. Principal Component Analysis (PCA)

1. Introduction to PCA.mp4

06:03

2. Conceptual Overview of PCA.mp4

13:15

3. Maximising Variance.mp4

16:24

4. Minimising Distances.mp4

08:45

5. Eigen Values & Eigen Vectors.mp4

11:13

6. PCA Summary.mp4

04:43

7. Understanding Eigen Values.mp4

10:38

8. PCA Code.mp4

11:49

9. Choosing the right dimensions.mp4

09:20

8. K-Nearest Neigbours

1. Introduction.mp4

03:04

2. KNN Idea.mp4

06:33

3. KNN Data Prep.mp4

03:27

4. KNN Algorithm Code.mp4

14:55

5. Euclidean and Manhattan Distance.mp4

03:29

6. Deciding value of K.mp4

01:54

7. KNN and Data Standardisation.mp4

01:28

8. KNN Pros and Cons.mp4

04:31

9. KNN using Sk-Learn.html

9. PROJECT - Face Recognition

1. OpenCV - Working with Images.mp4

06:19

2. OpenCV - Video Input from WebCam.mp4

06:45

3. Object Detection using Haarcascades.mp4

08:21

4. Face Detection in Images.mp4

09:48

5. Face Detection in Live Video.mp4

05:44

6. Face Recognition Project Intro.mp4

04:24

7. Face Recognition 01 - Data Collection.mp4

23:49

8. Face Recognition 02 - Loading Data.mp4

12:34

9. Face Recognition 03 - Predictions using KNN.mp4

09:54

10. K-Means

1. K-Means Algorithm.mp4

08:57

2. Code 01 - Data Prep.mp4

03:50

3. Code 02 - Init Centers.mp4

11:09

4. Code 03 - Assigning Points.mp4

11:38

5. Code 04 - Updating Centroids.mp4

08:29

6. Code 05 - Visualizing K-Means & Results.mp4

12:02

11. Project - Dominant Color Extraction

1. Introduction.mp4

01:41

2. Reading Images.mp4

04:21

3. Finding Clusters.mp4

10:22

4. Dominant Color Swatches.mp4

08:20

5. Image in K-Colors.mp4

09:11

12. Naive Bayes Algorithm

1. Bayes Theorem.mp4

06:09

2. Derivation of Bayes Theorem.mp4

06:25

3. Bayes Theorem Question.mp4

11:31

4. Naive Bayes Algorithm.mp4

07:12

5. Naive Bayes for Text Classification.mp4

13:14

6. Computing Likelihood.mp4

16:25

7.1 golf.csv

7. Understanding Golf Dataset.mp4

16:11

8. CODE - Prior Probability.mp4

05:06

9. CODE - Conditional Probability.mp4

08:40

10. CODE - Likelihood.mp4

13:58

11. CODE - Prediction.mp4

06:03

12. Implementing Naive Bayes - Sklearn.mp4

09:18

13. Multinomial Naive Bayes

1. Multinomial Naive Bayes.mp4

12:49

2. Laplace Smoothing.mp4

07:54

3. Multinomial Naive Bayes Example.mp4

14:27

4. Bernoulli Naive Bayes.mp4

13:54

5. Bernoulli Naive Bayes Example.mp4

10:34

6. Bias Variance Tradeoff.mp4

08:48

7. Gaussian Naive Bayes.mp4

09:04

8. CODE - Variants of Naive Bayes.mp4

07:48

14. PROJECT Spam Classifier

1. Project Overview.mp4

06:30

2. Data Clearning.mp4

13:35

3. WordCloud.mp4

08:08

4. Text Featurization.mp4

04:03

5. Model Building.mp4

03:35

6. Model Evaluation.mp4

06:18

15. Decision Trees

1. Decision Trees Introduction.mp4

06:26

2. Decision Trees Example.mp4

11:38

3. Entropy.mp4

11:04

4. CODE Entropy.mp4

06:26

5. Information Gain.mp4

17:55

6. CODE Split Data.mp4

12:12

7. CODE Information Gain.mp4

07:44

8. Construction of Decision Trees.mp4

06:11

9. Stopping Conditions.mp4

06:49

16. Decision Trees Implementation

1. CODE - Decision Tree Node.mp4

05:29

2. CODE - Train Decision Tree.mp4

11:01

3. CODE - Assign Target Variable to Each Node.mp4

05:15

4. CODE - Stopping Conditions.mp4

06:42

5. CODE - Train Child Nodes.mp4

07:11

6. CODE - Explore Decision Tree Model.mp4

08:09

7. CODE - Prediction.mp4

08:47

8. Handling Numeric Features.mp4

09:33

9. Bias Variance Tradeoff.mp4

05:58

10. Decision Trees for Regression.mp4

08:26

11. Decision Tree Code - Sklearn.mp4

02:55

17. PROJECT Titanic Survival Prediction

1.1 titanic train.csv

1. Project Overview.mp4

07:28

2. Exploratory Data Analysis.mp4

07:54

3. Exploratory Data Analysis - II.mp4

07:21

4. Data Preparation for ML Model.mp4

07:56

5. Handling Missing Values.mp4

09:52

6. Decision Tree Model Building.mp4

07:36

7. Visualize Decision Tree.mp4

08:49

18. Ensemble Learning Bagging

1. Ensemble Learning.mp4

05:59

2. Bagging Model.mp4

11:19

3. Why Bagging Helps.mp4

13:43

4. Random Forest Algorithm.mp4

09:43

5. Bias Variance Tradeoff.mp4

12:12

6. CODE Random Forest.mp4

09:38

19. Ensemble Learning Boosting

1. Boosting Introduction.mp4

07:55

2. Boosting Intuition.mp4

12:07

3. Boosting Mathematical Formulation.mp4

19:08

4. Concept of Pseudo Residuals.mp4

14:18

5. GBDT Algorithm.mp4

21:33

6. Bias Variance Tradeoff.mp4

07:16

7. CODE - Gradient Boosting Decision Trees.mp4

11:32

8. XGBoost.mp4

09:59

9. Adaptive Boosting (AdaBoost).mp4

10:24

20. PROJECT Customer Churn Prediction

1. Project Overview.mp4

10:34

2. Exploratory Data Analysis.mp4

09:27

3. Data Visualisation.mp4

05:05

4. Finding relations.mp4

06:29

5. Data Preparation.mp4

05:27

6. Model Building.mp4

06:31

7. Hyperparameter tuning.mp4

09:06

21. Deep Learning Introduction - Neural Network

1. Biological Neural Network.mp4

07:43

2. A Neuron.mp4

08:15

3. How does a perceptron Learns.mp4

07:59

4. Gradient Descent Updates.mp4

12:17

5. Neural Networks.mp4

12:50

6. 3 Layer NN.mp4

07:08

7. Why Neural Nets.mp4

10:48

8. Tensorflow Playground.mp4

11:57

9. CODE -Data Preparation.mp4

08:05

10. CODE - Model Building.mp4

09:20

11. CODE - Model Training and Testing.mp4

09:05

22. PROJECT Pokemon Image Classification

1.1 Dataset Link.html

1. Introduction.mp4

05:39

2. The Data.mp4

07:37

3. Structured Data.mp4

08:47

4. Data Loading.mp4

09:11

5. Data Preprocessing.mp4

10:20

6. Model Architecture.mp4

07:18

7. Softmax Function.mp4

05:41

8. Model Training.mp4

01:52

9. Model evaluation.mp4

07:47

10. Predictions.mp4

04:42

Description

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

What You'll Learn?

Jumpstart the world of AI & ML
Maths of Machine Learning
Regression & Classification Techniques
Linear & Logistic Regression
K-Nearest Neighbours, K-Means
Naive Bayes, Text Classification
Decision Trees & Random Forests
Ensemble Learning - Bagging & Boosting
Dimensionality Reduction
Neural Networks
8+ Hands on Projects

Who is this for?

Programmers who are curious to about Machine Learning and Artificial Intellgence

Working professionals who want to build a career in data science

Developers who wants to learn a new skill and build ML based projects

University and college students who want to strengthen their understanding of Machine Learning

More details

Description
Read to jumpstart the world of Machine Learning &Â Artificial intelligence?

This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions"
Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.
This comprehensive course is taught by Prateek Narang &Â Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years.

We are providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 8+ projects.

With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science.

Some of the topics that you will learn in this course.
Logistic Regression
Linear Regression
Principal Component Analysis
Naive Bayes
Decision Trees
Bagging and Boosting
K-NN
K-Means
Neural Networks

Some of the concepts that you will learn in this course.
Convex Optimisation
Overfitting vs Underfitting
Bias Variance Tradeoff
Performance Metrics
Data Pre-processing
Feature Engineering
Working with numeric data, images & textual data
Parametric vs Non-Parametric Techniques
Sign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!
Who this course is for:
Programmers who are curious to about Machine Learning and Artificial Intellgence
Working professionals who want to build a career in data science
Developers who wants to learn a new skill and build ML based projects
University and college students who want to strengthen their understanding of Machine Learning

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Machine Learning

Mohit Uniyal

Instructor's Courses

Mohit is a Data Scientist, programming instructor and co-creator at Coding Minutes. He has trained over 20000+ students in Machine Learning and AI over the last 3 years of his teaching experience. His expertise lies in Python, data science, machine learning and AI, and he has won many competitions. He has been doing AI-based projects for the last 4 years. He also leads the ML and Data Science Domain of Coding Minutes.

Prateek Narang

Instructor's Courses

Prateek is popular programming instructor and an ace software engineer having worked with Google in the past, currently working with Scaler and created Coding Minutes to bring high quality courses at pocket friendly pricing. He is known for his amazingly simplified explanations that makes everyone fall in love with programming. He has has over 5+ years of teaching experience and has trained over 50,000 students in classroom bootcamps & online course at a popular bootcamp in the past. His expertise lies in problem solving, algorithms, competitive programming and machine learning. His interactive mario style at prateeknarang resume is loved by all. Many of his ex-students are now working in top product companies like Apple, Google, Amazon, PayTm, Microsoft, Flipkart, Samsung, Adobe, DE Shaw, Codenation, Arcesium and more.

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