Development
Data Science
Machine Learning
Excelling in Machine Learning using Python
4.3 (28 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.
10,983 students enrolled

Excelling in Machine Learning using Python

Learning Supervised & Unsupervised ML algorithms and implementation in Python
New
4.3 (28 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.
10,983 students enrolled
Created by Manoj Chandak
Last updated 6/2020
English
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Excelling in Machine Learning using Python
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This course includes
  • 5.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Context of machine learning in today’s business world
  • Understanding machine learning life cycle
  • Techniques used in various phases of ML life cycle
  • Core understanding of ML algorithms
  • Implementation of ML algorithms
  • Model interpretation & optimization
Course content
Expand all 40 lectures 05:44:28
+ – Machine Learning Life cycle
8 lectures 59:35
Machine Learning Life cycle
Preview 06:38
Data Exploration
07:05
Implementing Data exploration Part 1
09:20
Implementing Data exploration Part 2 – Univariate Analysis
07:57
Implementing Data exploration Part 3 – Bivariate Analysis
07:18
Data Preprocessing
11:03
Confusion Matrix
05:40
Cross Validation
04:34
+ – Linear Regression Model
6 lectures 50:25
Linear Regression Model Part 1
Preview 07:59
Linear Regression Model Part 2 - Gradient descent
08:10
Linear Regression Model Evaluation
06:42
Implementing Linear Regression Model in Python
12:48
Implementing Linear Regression Model in Python - Additional Techniques
09:59
Implementing Multi-variable Regression Model in Python
04:47
+ – Logistic Regression Model
3 lectures 40:58
Logistic Regression Model
13:08
Implementing Logistic Regression Model in Python
18:13
Implementing Confusion Matrix and Cross Validation
09:37
+ – K Nearest Neighbor (KNN) Model
4 lectures 30:33
K Nearest Neighbor (KNN) Model
08:50
Implementing K Nearest Neighbor (KNN) Model
10:18
Implementing Pipeline in KNN Model
05:43
Optimizing value of K in KNN
05:42
+ – SVM Model
3 lectures 16:14
Support Vector Machine (SVM) Model
08:06
SVM Radial basis Function (RBF) Kernel
03:50
Implementing SVM Model in Python
04:18
+ – Naive Bayes Model
2 lectures 20:16
Naive Bayes Model
15:51
Implementing Naive Bayes Model in Python
04:25
+ – Decision Tree Model
3 lectures 36:22
Decision Tree Model
18:47
Implementing Decision Tree Model in Python
06:42
Visualizing Decision Tree Model
10:53
+ – Random Forest Model
4 lectures 33:47
Random Forest Model
10:30
Implementing Random Forest Model in Python
08:02
Optimization of Hyper parameters in Random Forest
10:38
Feature Importance in Random Forest
04:37
+ – K-means Clustering
3 lectures 28:01
K-means Clustering
10:14
Elbow method in K-means Clustering
05:42
Implementing K-means clustering in Python
12:05
Requirements
  • Understanding of Python programming
Description

Yes, you are exploring the right course in the exciting field of machine learning.

Let us find the reasons in this course – Why to learn ML?

Let us find the path of ML learning – What to learn in ML?

Let us find the way of ML learning – How to learn ML?

In my 28 years of experience in software field, machine learning is one of my most exciting techno- managerial area to work and teach. In my opinion this skill will be the need of most of the business stake holders in every field. Machine learning is the core component of Artificial Intelligence and Data Science.

That’s why, in this course we will be learning core concepts of various algorithms in in simple language. You need good understanding of algorithms/models for correct implementation of it. Also, that will help in effective Optimization, Interpretation and Communication of the output of the model to various stake holders.

In this course, you will understand the various steps of model implementation in Python.

This course lectures consists of many supervised and unsupervised algorithms like Regression, Logistic regression, KNN, SVM, Naïve Bayes, Decision Tree, Random Forest, K-Means, Hierarchical clustering, etc. with core concepts and Python implementation of various ML life cycle.

So are you thrilled…..then why are you waiting for…. Let us explore this course….

Who this course is for:
  • Executives, software developers, Analysts who are in the transition of evolving career of Machine learning
  • Students to jumpstart in exciting and lucrative future of Machine learning
  • Managers to upgrade their skills in this demanding Machine learning field
  • Academics for understanding core concepts of Machine learning