Master Machine Learning , Deep Learning with Python
4.0 (113 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,484 students enrolled

Master Machine Learning , Deep Learning with Python

Complete course covering fundamentals of Machine learning , Deep learning with Python code
4.0 (113 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,484 students enrolled
Last updated 6/2019
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This course includes
  • 5 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Machine Learning
Course content
Expand all 117 lectures 05:09:09
+ Introduction to Machine Learning fundamental concepts
10 lectures 40:17

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

Preview 05:04
What are cost functions
07:40
Regression and Classification
02:06
Labelled Data and Unlabelled data
03:06
Feature Weights
03:17
Machine Learning Framework
03:12
Training and Testing
03:26
Cross Validation
05:00

Evaluate yourself on what you have understood in Machine Learning Fundamentals

Quiz on Machine Learning Fundamentals
11 questions
+ Basic Statistics
2 lectures 05:10
Mean and Median
01:15
Standard Deviation
03:55
+ Feature Engineering
6 lectures 16:43

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

Preview 05:46

Understand  what one hot encoding is

One Hot Encoding
02:09

One hot encoding python code

One Hot Encoding - Code
01:17
Scaling - Why we need scaling
01:32
Normalization and Standardization
02:56
Normalization and Standardization Code
03:03

Evaluate your understanding of Feature Engineering

Feature Engineering Quiz
5 questions
+ Using Google Python Notebook.
3 lectures 04:02
Using Python Notebook for Machine Learning
02:07
Setting Up Google Python NoteBook
01:46
Numpy and Pandas Tutorial
00:09
+ Linear Regression
7 lectures 26:20
Linear Regression Theory
05:20
What do scores tell us
01:48
Cross Validation In Linear Regression
03:29
Which model to use in cross validation
01:32
Taking your model to production
03:11
Hyper parameter tuning and Cross Validation
00:17

Evaluate your understanding of  Linear Regression

Linear Regression Quiz
7 questions
+ Classification
15 lectures 33:06
True Positive and True Negative
02:31
False Negative and False Positive
02:01
Sensitivity
01:11
Specificity
01:58
True Positive,True Negative, False Positive, False Negative via graph
03:23
Sensitivity Via Graph
01:45
Specificity Via Graph
02:10
Sensitivity and Specificity Relationship
02:57
Specificity Not Same As Precision
00:18
ROC - Area Under Curve
02:31
Different ROC Curves
01:15
Confusion Matrix
02:53
Precision
02:18
Recall
02:02

Evaluate your understanding of classification

Classification Quiz
12 questions
+ KNN - K Nearest neighbours Algorithm
7 lectures 16:47
KNN for Classification
01:59
KNN for Regression
01:03
How to decide value of K
01:31
Euclidean Distance
01:21
KNN - Summary
00:40
KNN using SKLearn and Accuracy
08:05
Visualizing Data Using Pandas
02:08

Evaluate your understanding of KNN.

KNN Quiz
6 questions
+ Overfitting UnderFitting
3 lectures 09:03
Overfitting UnderFitting Bias and Variance
03:43
What is regularization
03:33
Regularization Rate Lamda
01:47

Evaluate your understanding of Overfitting and Underfitting

Overfitting and Underfitting Quiz
8 questions
+ Decision Trees
10 lectures 24:25
What are decision trees
03:27
Decision Tree Example
01:43
How a decision tree decides to split - Entropy
01:14
What is Entropy
00:51
Decision Tree Information Gain
01:04
Entropy Of Parent
01:39
Information Gain For Measurement -1
03:17
Information Gain For Measurement -2
02:33
Information Gain For Measurement -3
04:33
Decision Tree Using SKLearn
04:04

Evaluate your understanding of decision trees

Decision Tree Quiz
2 questions
Requirements
  • Basic Python, Numpy and Pandas
Description

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.


Who this course is for:
  • People interested about data science