Machine Learning (beginner to expert)

Uplatz offers this comprehensive course on Machine Learning. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Machine Learning provides machines with the ability to learn autonomously based on experiences, observations and analyzing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow.

Whereas in machine learning, we input a data set through which the machine will learn by identifying and analyzing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset. The first step in machine learning basics is that we feed knowledge/data to the machine; this data is divided into two parts namely, training data and testing data.

In this Machine Learning Basics Course you will -

• Get introduced to the world of machine learning with some basic concepts
• Statistics, Artificial Intelligence, Deep Learning and Data mining are few of the other technical words used with machine learning
• Learn about the different types of machine learning algorithms

At the end of the course you will receive a course completion certificate issued by Uplatz.

Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included

• Machine Learning (basic to adv) - all lectures 63:25:40

  • 1: 1. INTRODUCTION TO LINEAR ALGEBRA 32:52
  • 2: 1.1 INTRODUCTION TO LINEAR ALGEBRA 27:09
  • 3: 1.2 LINEAR ALGEBRA 1 13:55
  • 4: 1.2.1 LINEAR ALGEBRA 2 13:39
  • 5: 1.2.2 LINEAR ALGEBRA 3 11:21
  • 6: 1.2.3 LINEAR ALGEBRA 4.1 06:46
  • 7: 1.2.4 LINEAR ALGEBRA 4 08:40
  • 8: 1.2.5 LINEAR ALGEBRA 5.1 16:32
  • 9: 1.2.6 LINEAR ALGEBRA 5 16:45
  • 10: 1.2.7 LINEAR ALGEBRA 27:37
  • 11: 1.2.8 LINEAR ALGEBRA 16:27
  • 12: 1.2.9 LINEAR ALGEBRA 16:24
  • 13: 1.2.9.1 LINEAR ALGEBRA 25:27
  • 14: 1.2.9.2 LINEAR ALGEBRA 25:03
  • 15: 1.2.9.2.1 LINEAR ALGEBRA 16:45
  • 16: 2.1 INTRODUCTION TO PYTHON 16:19
  • 17: 2.1.1 INTRODUCTION TO PYTHON 19:20
  • 18: 2.1.2 PYTHON DATATYPES 18:51
  • 19: 2.1.3 PYTHON OPERATORS 21:37
  • 20: 2.1.4 ADVANCED DATA TYPES 18:40
  • 21: 2.1.5 SIMPLE PYTHON PROGRAM 17:47
  • 22: 2.1.6 PYTHON CONDITION STATEMENTS 25:44
  • 23: 2.1.7 PYTHON LOOPING STATEMENTS 16:48
  • 24: 2.1.8 - BREAK AND CONTINUE KEYWORDS IN PYTHON 20:49
  • 25: 2.1.9 FUNCTIONS IN PYTHON 16:53
  • 26: 2.1.9.1 FUNCTION ARGUMENTS 18:40
  • 27: 2.1.9.2 FUNCTION REQUIRED ARGUMENTS 10:36
  • 28: 2.1.9.3 DEFAULT ARGUMENTS 06:37
  • 29: 2.1.9.4 VARIABLE ARGUMENTS 32:18
  • 30: 2.2 BUILT IN FUNCTIONS 37:15
  • 31: 2.2.1 BUILT IN FUNCTIONS 09:45
  • 32: 2.2.2 SCOPE OF VARIABLES 13:15
  • 33: 2.2.3 PYTHON MATH MODULE 12:53
  • 34: 2.2.3.1 PYTHON MATH MODULE 18:40
  • 35: 2.2.4 PYTHON MATPLOTLIB MODULE 1:04:10
  • 36: 2.2.5 A BASIC GUI APPLICATION 33:37
  • 37: 2.2.6 A BASIC GUI APPLICATION 36:44
  • 38: 2.3 NUMPY BASICS 17:06
  • 39: 2.3.1 NUMPY BASICS 24:43
  • 40: 2.3.2 NUMPY BASICS 29:55
  • 41: 2.3.3 NUMPY BASICS 28:44
  • 42: 2.3.4 NUMPY BASICS 21:25
  • 43: 2.3.5 NUMPY BASICS 21:26
  • 44: 2.3.6 NUMPY BASICS 22:47
  • 45: 2.3.7 NUMPY BASICS 16:53
  • 46: 2.3.8 NUMPY BASICS 10:24
  • 47: 2.3.9 NUMPY BASICS 10:23
  • 48: 2.3.9.1 NUMPY BASICS 14:44
  • 49: 2.3.9.2 NUMPY BASICS 06:22
  • 50: 2.3.9.3 NUMPY BASICS 14:16
  • 51: 2.3.9.4 NUMPY BASICS 25:20
  • 52: 2.3.9.5 NUMPY BASICS 14:24
  • 53: 2.3.9.6 NUMPY BASICS 18:25
  • 54: 2.3.9.7 NUMPY BASICS 14:41
  • 55: 2.3.9.8 NUMPY BASICS 16:35
  • 56: 2.3.9.9 NUMPY BASICS 29:19
  • 57: 2.3.9.11 NUMPY BASICS 24:23
  • 58: 2.3.9.12 NUMPY BASICS 15:39
  • 59: 2.3.9.13 NUMPY BASICS 25:20
  • 60: 2.3.9.14 NUMPY BASICS 22:12
  • 61: 2.3.9.15 NUMPY BASICS 19:44
  • 62: 2.3.9.16 NUMPY BASICS 21:46
  • 63: 2.3.9.17 NUMPY BASICS 21:24
  • 64: 2.3.9.18 NUMPY BASICS 11:54
  • 65: 2.3.9.19 NUMPY BASICS 12:18
  • 66: 2.3.9.21 NUMPY BASICS 16:35
  • 67: 2.4 FILE SYSTEM 14:09
  • 68: 2.4.1 FILE SYSTEM WITH STATEMENT 23:38
  • 69: 2.4.2 FILE SYSTEM READ AND WRITE 28:56
  • 70: 2.5 RANDOM MODULE BASICS 14:49
  • 71: 2.5.1 RANDOM MODULE BASICS 19:02
  • 72: 2.5.2 RANDOM MODULE BASICS 22:42
  • 73: 2.5.3 RANDOM MODULE BASICS 22:42
  • 74: 2.5.4 RANDOM MODULE BASICS 10:04
  • 75: 2.5.5 RANDOM MODULE BASICS 09:21
  • 76: 2.5.6 RANDOM MODULE BASICS 43:00
  • 77: 2.6 PANDAS BASICS 15:57
  • 78: 2.6.1 PANDAS BASICS 05:54
  • 79: 2.6.2 PANDAS BASICS 32:00
  • 80: 2.6.3 PANDAS BASICS 20:59
  • 81: 2.6.4 PANDAS BASICS 28:10
  • 82: 2.6.5 PANDAS BASICS 15:19
  • 83: 2.6.6 PANDAS BASICS 08:09
  • 84: 2.6.7 PANDAS BASICS 18:57
  • 85: 2.7 MATPLOTLIB BASICS 20:23
  • 86: 2.7.1 MATPLOTLIB BASICS 20:38
  • 87: 2.7.2 MATPLOTLIB BASICS 17:32
  • 88: 2.7.3 MATPLOTLIB BASICS 04:00
  • 89: 2.7.4 MATPLOTLIB BASICS 11:32
  • 90: 2.7.5 MATPLOTLIB BASICS 07:24
  • 91: 2.7.6 MATPLOTLIB BASICS 16:55
  • 92: 2.7.7 MATPLOTLIB BASICS 11:54
  • 93: 2.7.8 MATPLOTLIB BASICS 17:17
  • 94: 2.7.9 MATPLOTLIB BASICS 17:40
  • 95: 2.7.9.1 MATPLOTLIB BASICS 16:55
  • 96: 2.7.9.11 MATPLOTLIB BASICS 20:38
  • 97: 2.8 AGE CALCULATOR APP 26:46
  • 98: 2.8.1 AGE CALCULATOR APP 12:22
  • 99: 2.8.2 AGE CALCULATOR APP 33:00
  • 100: 2.8.3 AGE CALCULATOR APP 37:57
  • 101: 3.1 MACHINE LEARNING BASICS 27:27
  • 102: 3.1.1 MACHINE LEARNING BASICS 17:31
  • 103: 3.1.2 MACHINE LEARNING BASICS 17:36
  • 104: 3.1.3 MACHINE LEARNING BASICS 15:39
  • 105: 3.1.4 MACHINE LEARNING BASICS 13:54
  • 106: 3.1.5 MACHINE LEARNING BASICS 11:56
  • 107: 3.1.6 MACHINE LEARNING BASICS 18:52
  • 108: 3.1.7 MACHINE LEARNING BASICS 23:56
  • 109: 3.1.8 MACHINE LEARNING BASICS 22:38
  • 110: 3.1.9 MACHINE LEARNING BASICS 29:13
  • 111: 3.1.9.1 MACHINE LEARNING BASICS 17:36
  • 112: 3.2 MACHINE LEARNING BASICS 08:09
  • 113: 4.1 TYPES OF MACHINE LEARNING 35:12
  • 114: 4.1.1 TYPES OF MACHINE LEARNING 15:03
  • 115: 4.1.2 TYPES OF MACHINE LEARNING 16:18
  • 116: 4.1.3 TYPES OF MACHINE LEARNING 13:58
  • 117: 4.1.4 TYPES OF MACHINE LEARNING 19:45
  • 118: 4.1.5 TYPES OF MACHINE LEARNING 05:13
  • 119: 4.1.6 TYPES OF MACHINE LEARNING 31:39
  • 120: 5.1 TYPES OF MACHINE LEARNING 28:20
  • 121: 5.1.1 TYPES OF MACHINE LEARNING 31:56
  • 122: 5.1.2 TYPES OF MACHINE LEARNING 25:09
  • 123: 5.1.3 TYPES OF MACHINE LEARNING 46:37
  • 124: 5.1.4 TYPES OF MACHINE LEARNING 31:00
  • 125: 5.1.5 TYPES OF MACHINE LEARNING 24:22
  • 126: 5.1.6 TYPES OF MACHINE LEARNING 16:20
  • 127: 5.1.7 TYPES OF MACHINE LEARNING 32:54
  • 128: 5.1.8 TYPES OF MACHINE LEARNING 56:20
  • 129: 5.2 MULTIPLE REGRESSION 34:30
  • 130: 5.2.1 MULTIPLE REGRESSION 37:36
  • 131: 5.2.2 MULTIPLE REGRESSION 40:56
  • 132: 5.2.3 MULTIPLE REGRESSION 56:04
  • 133: 5.2.4 MULTIPLE REGRESSION 46:42
  • 134: 5.2.5 MULTIPLE REGRESSION 38:14
  • 135: 5.2.6 MULTIPLE REGRESSION 37:49
  • 136: 5.2.7 MULTIPLE REGRESSION 1:01:26
  • 137: 5.3 KNN INTRO 26:49
  • 138: 5.3.1 KNN ALGORITHM 48:57
  • 139: 5.3.2 KNN ALGORITHM 11:17
  • 140: 5.3.3 INTRODUCTION TO CONFUSION MATRIX 42:22
  • 141: 5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT 24:37
  • 142: 5.3.5 KNN ALGORITHM 50:29
  • 143: 5.3.6 KNN ALGORITHM 56:11
  • 144: 5.4 INTRODUCTION TO DECISION TREE 44:37
  • 145: 5.4.1 INTRODUCTION TO DECISION TREE 39:32
  • 146: 5.4.2 DECISION TREE ALGORITHM 36:42
  • 147: 5.4.3 DECISION TREE ALGORITHM 20:11
  • 148: 5.4.4 DECISION TREE ALGORITHM 55:38
  • 149: 5.5 UNSUPERVISED LEARNING 23:26
  • 150: 5.5.1 UNSUPERVISED LEARNING 09:17
  • 151: 5.5.2 UNSUPERVISED LEARNING 18:29
  • 152: 5.5.3 UNSUPERVISED LEARNING 29:50
  • 153: 5.5.4 AHC ALGORITHM 46:31
  • 154: 5.5.5 AHC ALGORITHM 19:56
  • 155: 5.6 KMEANS CLUSTERING 23:09
  • 156: 5.6.1 KMEANS CLUSTERING 30:26
  • 157: 5.6.2 KMEANS CLUSTERING 1:01:05
  • 158: 5.6.3 DBSCAN ALGORITHM 37:09
  • 159: 5.6.4 DBSCAN PROGRAM 32:45
  • 160: 5.6.5 DBSCAN PROGRAM 49:57

Machine Learning Basics – Course Syllabus

Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. Artificial Intelligence | An Introduction
9. Machine Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions

Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Feature Scaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Supervised learning :
1. Getting started with Classification
2. Basic Concept of Classification
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Gradient Descent :
• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer

8. Linear Regression :
• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple Linear Regression using Python
• Multiple Linear Regression using R
• Locally weighted Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression

9. Python | Implementation of Polynomial Regression

10. Softmax Regression using TensorFlow

11. Logistic Regression :
• Understanding Logistic Regression
• Why Logistic Regression in Classification ?
• Logistic Regression using Python
• Cost function in Logistic Regression
• Logistic Regression using Tensorflow

12. Naive Bayes Classifiers

13. Support Vector:
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Support Vector Machines(SVMs) in R
• Using SVM to perform classification on a non-linear dataset

14. Decision Tree:
• Decision Tree
• Decision Tree Regression using sklearn
• Decision Tree Introduction with example
• Decision tree implementation using Python
• Decision Tree in Software Engineering

15. Random Forest:
• Random Forest Regression in Python
• Ensemble Classifier
• Voting Classifier using Sklearn
• Bagging classifier

Everyone

Passion to achieve your goals

• Machine Learning Associate
• Machine Learning Engineer
• Machine Learning Research Engineer

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