About this Course

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Shareable Certificate
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Course 4 of 6 in the
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Intermediate Level
Approx. 30 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 30 hours to complete
English
Subtitles: English

Offered by

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IBM

Syllabus - What you will learn from this course

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Week
1

Week 1

5 hours to complete

Tensor and Datasets

5 hours to complete
6 videos (Total 44 min), 1 reading, 11 quizzes
6 videos
1.1 Tensors 1D13m
1.2 Two-Dimensional Tensors9m
Differentiation in PyTorch5m
1.3 Simple Dataset7m
1.5 Dataset4m
1 reading
Labs10m
5 practice exercises
1.1 Tensors 1D5m
1.2 Two-Dimensional Tensors5m
1.3 Derivatives in PyTorch5m
Simple Dataset5m
Datasets10m
Week
2

Week 2

2 hours to complete

Linear Regression

2 hours to complete
7 videos (Total 35 min)
7 videos
2.1 Linear Regression Training3m
Loss3m
Gradient Descent4m
Cost3m
Linear Regression PyToch5m
PyTorch Linear Regression Training Slope and Bias5m
7 practice exercises
Prediction in One Dimension5m
Linear Regression Training5m
Loss5m
Gradient Descent5m
Cost5m
Training Parameters in PyTorch5m
PyTorch Linear Regression Training Slope and Bias5m
3 hours to complete

Linear Regression PyTorch Way

3 hours to complete
5 videos (Total 21 min)
5 videos
Mini-Batch Gradient Descent3m
Optimization in PyTorch3m
Training, Validation and Test Split4m
Training, Validation and Test Split PyTorch3m
4 practice exercises
Quiz: Stochastic Gradient Descent5m
Mini-Batch Gradient Descent5m
3.3 Optimization in PyTorch5m
Training and Validation Data PyTorch5m
Week
3

Week 3

2 hours to complete

Multiple Input Output Linear Regression

2 hours to complete
4 videos (Total 18 min)
4 videos
Multiple Linear Regression Training2m
Linear Regression Multiple Outputs5m
Multiple Output Linear Regression Training1m
2 practice exercises
Multiple Linear Regression Prediction5m
Multiple Output Linear Regression5m
2 hours to complete

Logistic Regression for Classification

2 hours to complete
4 videos (Total 31 min)
4 videos
5.1 Logistic Regression: Prediction6m
Bernoulli Distribution and Maximum Likelihood Estimation5m
Logistic Regression Cross Entropy Loss10m
5 practice exercises
5.0 Linear Classifiers5m
5.0 Linear Classifiers5m
5.1 Logistic Regression: Prediction10m
Bernoulli Distribution and Maximum Likelihood Estimation5m
5.3 Logistic Regression Cross Entropy Loss10m
Week
4

Week 4

2 hours to complete

Softmax Rergresstion

2 hours to complete
3 videos (Total 18 min)
3 videos
6.2 Softmax Function:Using Lines to Classify Data3m
Softmax PyTorch6m
3 practice exercises
6.1 Softmax Function:Using Lines to Classify Data5m
6.2 Softmax Prediction5m
6.3 Softmax PyTorch Quizz5m
3 hours to complete

Shallow Neural Networks

3 hours to complete
6 videos (Total 33 min)
6 videos
More Hidden Neurons2m
Neural Networks with Multiple Dimensional Input5m
7.4 Multi-Class Neural Networks5m
7.5 Backpropagation5m
7.5 Activation Functions4m
6 practice exercises
Neural Networks5m
More Hidden Neurons 5m
Neural Networks with Multiple Dimensional Inputs5m
Multi-Class Neural Networks5m
Backpropagation5m
Activation Functions5m

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About the IBM AI Engineering Professional Certificate

The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders. By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer....
IBM AI Engineering

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