Master Machine Learning , Deep Learning with Python

Master Machine Learning , Deep Learning with Python, available at $19.99, has an average rating of 3.65, with 124 lectures, 10 quizzes, based on 125 reviews, and has 6506 subscribers.

You will learn about Machine Learning This course is ideal for individuals who are People interested about data science It is particularly useful for People interested about data science.

Enroll now: Master Machine Learning , Deep Learning with Python

Summary

Title: Master Machine Learning , Deep Learning with Python

Price: $19.99

Average Rating: 3.65

Number of Lectures: 124

Number of Quizzes: 10

Number of Published Lectures: 117

Number of Published Quizzes: 10

Number of Curriculum Items: 134

Number of Published Curriculum Objects: 127

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning

Who Should Attend

People interested about data science

Target Audiences

People interested about data science

Let me begin by telling secrets of mastery of machine learning.

# Secret 1 – The overall secret is machine learning is to know what not to learn. Given the amount of information in machine learning it is important to focus on important concepts and not get distracted.

#Secret 2 –The requirement of maths and statistics is very shallow.? In general people think that to? master machine learning one needs to know lot of maths and statistics. That is not true. When it comes to applying machine learning, the knowledge of maths and statistics is limited.? The way to think about this to compare with knowledge of database indexes. You need to master the best practices of using database indexes. You don’t need to know how databases indexes algorithms work. The same holds for machine learning concepts.

#Secret 3? – The key skill to master machine learning is fine tuning. Any experienced ML expert will tell you that the maximum time that goes in taking machine learning problems to production? is optimisation. Hence ,is important to understand terms like overfitting ,underfitting sensitivity, specificity, precision, ROC, AUC. The course spends lot of time on these key fundamental concepts.

Also the likes of Google and? Amazon are producing tools like AutoML where the requirement of coding is close to? zero. But what is still required are the fundamental concepts. The world of tomorrow of data science is less of coding but more key concepts.

A journey of thousand miles begins with first step. You always wanted to learn machine learning but many factors stopped you – fear of Maths , Statistics , the complexity of subject. Today is the day to break away from those fears.

Enrol in the? machine learning course and see for yourself that mastering machine learning can be simplified.? Following are topics the course covers. The course uses Google Python notebooks. You see the code results immediately.

Fundamentals of machine learning –? Cost Functions, Labelled and Unlabelled data, Feature weights, Training and Testing Cross Validation.

Feature Engineering – Normalization, Standardization

Linear Regression

Classification –? Concepts about True Positive, True Negative, Sensitivity, Specificity, Precision, ROC, AUC, Confusion Matrix

KNN – Algorithm

OverFitting and UnderFitting

Regularization

Decision Trees – Entropy, Information Gain

Bagging and Boosting

Unsupervised Learning – K-Means

Deep Learning – Weights, Bias, Epochs, Gradient Descent,Batch, Stochastic Gradient Descent , Mini Batch

Appendix course on Numpy and Pandas have also been added.

Following are essential points before taking the course

A good knowledge of Python, Numpy and Pandas? is required. Please don’t proceed with the course unless you master it.

You need to be patient. Please be prepared to spend two to? four months to digest these concepts if you are completely new to machine learning.

Course Curriculum

Chapter 1: Preparing Psychologically

Lecture 1: Preparing Psychologically

Chapter 2: Introduction to Machine Learning fundamental concepts

Lecture 1: Difference between AI, Machine Learning and Deep Learning

Lecture 2: How should one approach machine Learning

Lecture 3: How do machines really learn

Lecture 4: What are cost functions

Lecture 5: Regression and Classification

Lecture 6: Labelled Data and Unlabelled data

Lecture 7: Feature Weights

Lecture 8: Machine Learning Framework

Lecture 9: Training and Testing

Lecture 10: Cross Validation

Chapter 3: Basic Statistics

Lecture 1: Mean and Median

Lecture 2: Standard Deviation

Chapter 4: Feature Engineering

Lecture 1: Feature Engineering

Lecture 2: One Hot Encoding

Lecture 3: One Hot Encoding – Code

Lecture 4: Scaling – Why we need scaling

Lecture 5: Normalization and Standardization

Lecture 6: Normalization and Standardization Code

Chapter 5: Using Google Python Notebook.

Lecture 1: Using Python Notebook for Machine Learning

Lecture 2: Setting Up Google Python NoteBook

Lecture 3: Numpy and Pandas Tutorial

Chapter 6: Linear Regression

Lecture 1: Linear Regression Theory

Lecture 2: Linear Regression Code

Lecture 3: What do scores tell us

Lecture 4: Cross Validation In Linear Regression

Lecture 5: Which model to use in cross validation

Lecture 6: Taking your model to production

Lecture 7: Hyper parameter tuning and Cross Validation

Chapter 7: Classification

Lecture 1: Classification Problems

Lecture 2: True Positive and True Negative

Lecture 3: False Negative and False Positive

Lecture 4: Sensitivity

Lecture 5: Specificity

Lecture 6: True Positive,True Negative, False Positive, False Negative via graph

Lecture 7: Sensitivity Via Graph

Lecture 8: Specificity Via Graph

Lecture 9: Sensitivity and Specificity Relationship

Lecture 10: Specificity Not Same As Precision

Lecture 11: ROC – Area Under Curve

Lecture 12: Different ROC Curves

Lecture 13: Confusion Matrix

Lecture 14: Precision

Lecture 15: Recall

Chapter 8: KNN – K Nearest neighbours Algorithm

Lecture 1: KNN for Classification

Lecture 2: KNN for Regression

Lecture 3: How to decide value of K

Lecture 4: Euclidean Distance

Lecture 5: KNN – Summary

Lecture 6: KNN using SKLearn and Accuracy

Lecture 7: Visualizing Data Using Pandas

Chapter 9: Overfitting UnderFitting

Lecture 1: Overfitting UnderFitting Bias and Variance

Lecture 2: What is regularization

Lecture 3: Regularization Rate Lamda

Chapter 10: Decision Trees

Lecture 1: What are decision trees

Lecture 2: Decision Tree Example

Lecture 3: How a decision tree decides to split – Entropy

Lecture 4: What is Entropy

Lecture 5: Decision Tree Information Gain

Lecture 6: Entropy Of Parent

Lecture 7: Information Gain For Measurement -1

Lecture 8: Information Gain For Measurement -2

Lecture 9: Information Gain For Measurement -3

Lecture 10: Decision Tree Using SKLearn

Chapter 11: Bagging and Boosting

Lecture 1: Ensembling

Lecture 2: Ensembling -Code

Lecture 3: Bagging

Lecture 4: Bagging Code

Lecture 5: Random Forest Code

Lecture 6: Random Forest

Lecture 7: Boosting

Lecture 8: ADA Boost Code

Chapter 12: Unsupervised Learning

Lecture 1: What is unsupervised learning

Lecture 2: Clustering distance measurement

Lecture 3: Clustering algorithms type

Lecture 4: How does K- Means work

Lecture 5: K-Means Code

Lecture 6: Types of Hierarchical clustering

Lecture 7: Distance between clusters

Lecture 8: Single Linkage Method

Instructors

Vishal Kumar Singh
Demystifying Machine Learning

Rating Distribution

1 stars: 4 votes

2 stars: 7 votes

3 stars: 22 votes

4 stars: 46 votes

5 stars: 46 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!