4.09 out of 5
4.09
67 reviews on Udemy

Master Machine Learning , Deep Learning with Python

Complete course covering fundamentals of Machine learning , Deep learning with Python code
Instructor:
Vishal Kumar Singh
5,364 students enrolled
English [Auto-generated]
Machine Learning

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.

Introduction to Machine Learning fundamental concepts

1
Difference between AI, Machine Learning and Deep Learning

What is the difference between AI, Machine Learning and Deep Learning

2
How should one approach machine Learning
3
How do machines really learn
4
What are cost functions
5
Regression and Classification
6
Labelled Data and Unlabelled data
7
Feature Weights
8
Machine Learning Framework
9
Training and Testing
10
Cross Validation
11
Quiz on Machine Learning Fundamentals

Evaluate yourself on what you have understood in Machine Learning Fundamentals

Basic Statistics

1
Mean and Median
2
Standard Deviation

Feature Engineering

1
Feature Engineering

What is feature engineering and why it is the most important concept in machine learning

2
One Hot Encoding

Understand  what one hot encoding is

3
One Hot Encoding - Code

One hot encoding python code

4
Scaling - Why we need scaling
5
Normalization and Standardization
6
Normalization and Standardization Code
7
Feature Engineering Quiz

Evaluate your understanding of Feature Engineering

Using Google Python Notebook.

1
Using Python Notebook for Machine Learning
2
Setting Up Google Python NoteBook
3
Numpy and Pandas Tutorial

Linear Regression

1
Linear Regression Theory
2
Linear Regression Code
3
What do scores tell us
4
Cross Validation In Linear Regression
5
Which model to use in cross validation
6
Taking your model to production
7
Hyper parameter tuning and Cross Validation
8
Linear Regression Quiz

Evaluate your understanding of  Linear Regression

Classification

1
Classification Problems
2
True Positive and True Negative
3
False Negative and False Positive
4
Sensitivity
5
Specificity
6
True Positive,True Negative, False Positive, False Negative via graph
7
Sensitivity Via Graph
8
Specificity Via Graph
9
Sensitivity and Specificity Relationship
10
Specificity Not Same As Precision
11
ROC - Area Under Curve
12
Different ROC Curves
13
Confusion Matrix
14
Precision
15
Recall
16
Classification Quiz

Evaluate your understanding of classification

KNN - K Nearest neighbours Algorithm

1
KNN for Classification
2
KNN for Regression
3
How to decide value of K
4
Euclidean Distance
5
KNN - Summary
6
KNN using SKLearn and Accuracy
7
Visualizing Data Using Pandas
8
KNN Quiz

Evaluate your understanding of KNN.

Overfitting UnderFitting

1
Overfitting UnderFitting Bias and Variance
2
What is regularization
3
Regularization Rate Lamda
4
Overfitting and Underfitting Quiz

Evaluate your understanding of Overfitting and Underfitting

Decision Trees

1
What are decision trees
2
Decision Tree Example
3
How a decision tree decides to split - Entropy
4
What is Entropy
5
Decision Tree Information Gain
6
Entropy Of Parent
7
Information Gain For Measurement -1
8
Information Gain For Measurement -2
9
Information Gain For Measurement -3
10
Decision Tree Using SKLearn
11
Decision Tree Quiz

Evaluate your understanding of decision trees

Bagging and Boosting

1
Ensembling
2
Ensembling -Code
3
Bagging
4
Bagging Code
5
Random Forest Code
6
Random Forest
7
Boosting
8
ADA Boost Code
9
Bagging and Boosting Quiz

Evaluate your understanding of Bagging and Boosting

Unsupervised Learning

1
What is unsupervised learning
2
Clustering distance measurement
3
Clustering algorithms type
4
How does K- Means work
5
K-Means Code
6
Types of Hierarchical clustering
7
Distance between clusters
8
Single Linkage Method
9
Hierarchical Clustering Code
10
Unsupervised Learning Quiz
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Includes

5 hours on-demand video
2 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion