Machine Learning in Python for Absolute Beginners

Eduonix Learning Solutions Pvt Ltd

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• Course Introduction 02:18

  • 1: Course Introduction 02:18

• Introduction to Machine Learning with Python 30:54

  • 2: Section Introduction 00:59
  • 3: What is Machine Learning? 03:57
  • 4: Types of Machine Learning 12:32
  • 5: Applications of Machine Learning 08:45
  • 6: Setting up the dev environment 01:41
  • 7: Summary 03:00

• Data Preparation 29:31

  • 8: Section Introduction 01:06
  • 9: Loading data sets 02:55
  • 10: Preprocessing text data 04:24
  • 11: Data Cleaning 04:07
  • 12: Handling the Missing Data 03:03
  • 13: Handling the Noisy Data 03:08
  • 14: Data Transformation 03:17
  • 15: Data Reduction 03:36
  • 16: Data Integration 02:07
  • 17: Summary 01:48

• Evaluation and tuning of Classification Models 43:00

  • 18: Section Introduction 00:47
  • 19: Introduction to Validation Techniques 02:22
  • 20: Resubstitution and Hold-out 02:47
  • 21: K-fold Cross-Validation 02:54
  • 22: Leave-One-Out-Cross-Validation 03:00
  • 23: Random Sub- Sampling and Bootstrapping 01:35
  • 24: Bias 01:54
  • 25: Variance 01:31
  • 26: Underfitting and Overfitting 08:58
  • 27: Hyperparameter tuning 03:23
  • 28: Implementing Hyperparameter Tuning 10:33
  • 29: Visualizing model results 02:09
  • 30: Summary 01:07

• Supervised Learning 1 - Regression 22:56

  • 31: Section Introduction 00:32
  • 32: Introduction to Linear regression 02:51
  • 33: Model evaluation and interpretation of results 02:10
  • 34: Important Metrics 05:53
  • 35: Confusion Matrix 00:58
  • 36: Multiple Linear regression 03:17
  • 37: Non linear regression 01:24
  • 38: Regression on Iris Data Set 04:19
  • 39: Summary 01:32

• Supervised Learning 2- Classification 23:28

  • 40: Section 5 Introduction 00:41
  • 41: Introduction to Classification algorithms - Part 1 04:38
  • 42: Introduction to Classification algorithms - Part 2 04:22
  • 43: Different types of Classification Algorithms 05:56
  • 44: Coding up a simple classification model using Decision Trees 05:00
  • 45: Coding up a simple classification model using Naive Bayes 01:59
  • 46: Summary 00:52

• Supervised Learning 3- Classification 12:54

  • 47: Section Introduction 00:38
  • 48: Introduction to Logistic Regression - Logistic vs. Linear Regression 03:09
  • 49: Introduction to Random Forest models 02:49
  • 50: Coding up a simple classification model using random forest 04:13
  • 51: Coding up a simple classification model using logistic regression 01:31
  • 52: Summary 00:34

• Supervised Learning 4 - Classification 14:36

  • 53: Section Introduction 00:41
  • 54: Introduction to K-nearest neighbors 02:26
  • 55: Introduction to SVM 04:38
  • 56: Coding up models using k-nearest neighbors 04:20
  • 57: Coding up models using SVM 01:36
  • 58: Summary 00:55

• Case studies from real world companies 25:06

  • 59: Case studies from real world companies -Part 1 00:35
  • 60: Case studies from real world companies -Part 2 06:51
  • 61: Credit Card Fraud Case 01:07
  • 62: Traffic prediction using machine learning 05:54
  • 63: Customer Behavior Analysis 04:36
  • 64: Fake news detection 05:09
  • 65: Summary 00:54

This beginner program will help anyone who wants to quickly start working on machine learning solutions. This program will teach the concepts using real-world problems.

Let's Have A Look At The Major Topics We'll Be Covering In This Course!

• Introduction to Machine Learning with Python

• Data Preparation

• Evaluation and tuning of Classification Models

• Supervised Learning - Regression and Classification

In this course, we'll take you through the topics of supervised learning and unsupervised learning. Also, you'll learn about the different algorithms like regression, naive Bayes, decision trees, logistic regression, random forest, KNN, and Support Vector Machines (SVM).

You'll be learning how to implement the following steps to successfully build machine learning models using Python

• Installing the Python and libraries

• Loading the dataset

• Summarizing the dataset

• Visualizing the dataset

• Evaluating some algorithms

• Making some predictions

Enroll today and learn the most in-demand skills of Python and machine learning

See You In The Class!

Any one who wants to start learning machine learning will find this course very useful

Basic programming knowledge is must for taking the course

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