Master in Artificial Intelligence (AI)
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Want to become an Successful AI Engineer but don’t know what to do and how?
Take a look at this course where you will
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Not only learn about the Artificial Intelligence and role of AI Engineer but also
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How to develop and deploy machine learning and deep learning models to address complex business issues and
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Become a Successful AI Engineer
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Preview many lectures for free to see the content for yourself
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Clear your doubts on this topic any time while doing the course
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Get Udemy’s 30 days Money Back Guarantee
My exposure to Artificial Intelligence began in 2020 when the demand for the AI Engineer role started increasing globally at a rapid pace
I went about understanding the AI Engineer job requirements from the industry to meet the growing demand for this emerging role and had a chance to prepare many students from a company I was working with in this domain
During these years, I learnt all about Artificial Intelligence that can help develop and deploy the machine learning and deep learning models to address specific business problems
I bring in this course my learnings from this journey and share with you how can you also become a Successful AI Engineer
Preview for yourself many lectures free. If you like the content, enroll for the course, enjoy and skill yourself to become a Master in Artificial Intelligence! If don’t like the content, please message about how can we modify it to meet your expectations.
Please remember that this course comes with Udemy’s 30 days Money Back Guarantee
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2
Overview IAt the end of this lecture, you will learn the following
What is Artificial Intelligence?
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3
Overview IPlease answer following questions based on learnings in this lecture
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4
Overview II
At the end of this lecture, you will learn the following
•What is Artificial Intelligence and career opportunities in this field
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5
Overview II
Please answer following questions based on learnings in this lecture
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6
Overview III
At the end of this lecture, you will learn the following
•What are the responsibilities of a AI Engineer?
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7
Overview III
Please answer following questions based on learnings in this lecture
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8
Problem Definition I
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
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9
Problem Definition I
Please answer following questions based on learnings in this lecture
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10
Problem Definition II
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
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11
Problem Definition II
Please answer following questions based on learnings in this lecture
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12
Problem Definition III
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
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13
Problem Definition III
Please answer following questions based on learnings in this lecture
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14
Problem Definition IV
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
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15
Problem Definition IV
Please answer following questions based on learnings in this lecture
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16
Data Collection & Preprocessing I
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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17
Data Collection & Preprocessing I
Please answer following questions based on learnings in this lecture
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18
Data Collection & Preprocessing II
At the end of this lecture, you will learn the following
How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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19
Data Collection & Preprocessing II
Please answer following questions based on learnings in this lecture
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20
Data Collection & Preprocessing III
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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21
Data Collection & Preprocessing III
Please answer following questions based on learnings in this lecture
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22
Data Collection & Preprocessing IV
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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23
Data Collection & Preprocessing IV
Please answer following questions based on learnings in this lecture
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24
Data Collection & Preprocessing V
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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25
Data Collection & Preprocessing V
Please answer following questions based on learnings in this lecture
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26
Data Collection & Preprocessing VI
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
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27
Data Collection & Preprocessing VI
Please answer following questions based on learnings in this lecture
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28
Algorithm Selection & Development I
At the end of this lecture, you will learn the following
How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
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29
Algorithm Selection & Development I
Please answer following questions based on learnings in this lecture
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30
Algorithm Selection & Development II
At the end of this lecture, you will learn the following
•How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
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31
Algorithm Selection & Development II
Please answer following questions based on learnings in this lecture
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32
Algorithm Selection & Development III
At the end of this lecture, you will learn the following
•How to determine type of output and evaluation metrices- Regression and Clustering
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33
Algorithm Selection & Development III
Please answer following questions based on learnings in this lecture
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34
Algorithm Selection & Development IV
At the end of this lecture, you will learn the following
•How does Silhouette Score measures how similar an object is to its own cluster compared to other clusters
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35
Algorithm Selection & Development IV
Please answer following questions based on learnings in this lecture
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36
Algorithm Selection & Development V
At the end of this lecture, you will learn the following
•How does Davies-Bouldin Index compute the average similarity between each cluster and its most similar cluster
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37
Algorithm Selection & Development V
Please answer following questions based on learnings in this lecture
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38
Algorithm Selection & Development VI
At the end of this lecture, you will learn the following
•How to does Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI) measure the agreement between true labels and cluster assignments
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39
Algorithm Selection & Development VI
Please answer following questions based on learnings in this lecture
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40
Algorithm Selection & Development VII
At the end of this lecture, you will learn the following
•Data Understanding and Preparation
•Researching Algorithms and Architectures
•Model Selection and Evaluation
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41
Algorithm Selection & Development VII
Please answer following questions based on learnings in this lecture
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42
Algorithm Selection & Development VIII
At the end of this lecture, you will learn the following
•Learning rate in gradient descent hyperparameter
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43
Algorithm Selection & Development VIII
Please answer following questions based on learnings in this lecture
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44
Algorithm Selection & Development IX
At the end of this lecture, you will learn the following
•Number of hidden layers in a neural network hyperparameter
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45
Algorithm Selection & Development IX
Please answer following questions based on learnings in this lecture
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46
Algorithm Selection & Development X
At the end of this lecture, you will learn the following
•Hyperparameter tuning
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47
Algorithm Selection & Development X
Please answer following questions based on learnings in this lecture
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48
Algorithm Selection & Development XI
At the end of this lecture, you will learn the following
•How to compare the performance of different models and architectures to identify the most effective ones
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49
Algorithm Selection & Development XI
Please answer following questions based on learnings in this lecture
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50
Algorithm Selection & Development XII
At the end of this lecture, you will learn the following
•How to Iterate on the model development process by fine-tuning hyperparameters
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51
Algorithm Selection & Development XII
Please answer following questions based on learnings in this lecture
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52
Algorithm Selection & Development XIII
At the end of this lecture, you will learn the following
•How to use techniques like regularization, dropout, batch normalization, and learning rate scheduling to improve model generalization and performance
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53
Algorithm Selection & Development XIII
Please answer following questions based on learnings in this lecture
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54
Algorithm Selection & Development XIV
At the end of this lecture, you will learn the following
•How to monitor and analyze model training/validation metrics
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55
Algorithm Selection & Development XIV
Please answer following questions based on learnings in this lecture
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56
Algorithm Selection & Development XV
At the end of this lecture, you will learn the following
•How to consider the interpretability and explainability of the selected models
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57
Algorithm Selection & Development XV
Please answer following questions based on learnings in this lecture
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Algorithm Selection & Development XVI
At the end of this lecture, you will learn the following
•How to train Decision trees algorithm for getting feature importance
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Algorithm Selection & Development XVI
Please answer the following questions based on learnings in this lecture
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Algorithm Selection & Development XVII
•How to train Random Forests algorithm for getting feature importance
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Algorithm Selection & Development XVII
Please answer following questions based on learnings in this lecture
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Algorithm Selection & Development XVIII
At the end of this lecture, you will learn the following
How to train Gradient boosting machines algorithm for getting feature importance
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63
Algorithm Selection & Development XVIII
Please answer following questions based on learnings in this lecture
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64
Algorithm Selection & Development XIX
At the end of this lecture, you will learn the following
•How to use feature importance analysis to provide insights into model predictions
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65
Algorithm Selection & Development XIX
Please answer following questions based on learnings in this lecture
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66
Algorithm Selection & Development XX
At the end of this lecture, you will learn the following
•What are Model Interpretability Methods to consider the interpretability and explainability of the selected models
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67
Algorithm Selection & Development XX
Please answer following questions based on learnings in this lecture
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68
Algorithm Selection & Development XXI
At the end of this lecture, you will learn the following
•What are attention mechanisms in deep learning models to consider the interpretability and explainability of the selected models
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Algorithm Selection & Development XXI
Please answer following questions based on learnings in this lecture
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Algorithm Selection & Development XXII
At the end of this lecture, you will learn the following
•Deploy the trained model in a production environment and integrate it into the application workflow.
•Implement monitoring and logging mechanisms to track model performance, drift, and errors over time.
•Continuously evaluate and update the model as new data becomes available or the problem requirements change
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71
Algorithm Selection & Development XXII
Please answer following questions based on learnings in this lecture
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72
Algorithm Selection & Development XXIII
At the end of this lecture, you will learn the following
•An example of researching, selecting, and developing appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data
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73
Feature Engineering I
At the end of this lecture, you will learn the following
•How to identify and extract meaningful features from the data to improve the performance of machine learning models
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74
Feature Engineering I
Please answer following questions based on learnings in this lecture
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75
Feature Engineering II
At the end of this lecture, you will learn the following
How to engineer new features or transform existing features
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76
Feature Engineering II
Please answer following question based on learnings in this lecture
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77
Feature Engineering III
At the end of this lecture, you will learn the following
How to select a subset of the most relevant features
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78
Feature Engineering III
Please answer following questions based on learnings in this lecture
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79
Feature Engineering IV
At the end of this lecture, you will learn the following
How to reduce the dimensionality of the feature space while preserving as much relevant information as possible
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80
Feature Engineering IV
Please answer the following questions based on learnings in this lecture
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81
Feature Engineering V
At the end of this lecture, you will learn the following
Remaining steps of feature engineering
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82
Feature Engineering V
Please answer following questions based on learnings in this lecture
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83
Feature Engineering VI
At the end of this lecture, you will learn the following
An example of identifying and extracting meaningful features from a dataset to improve the performance of a machine learning model
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84
Deployment I
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Model Serialization
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85
Deployment I
Please answer following questions based on learnings in this lecture
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86
Deployment II
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Remaining steps
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87
Deployment II
Please answer following questions based on learnings in this lecture
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88
Deployment III
•An example of deploying trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements
