Rating 3.8 out of 5 (25 ratings in Udemy)
What you'll learn- Artificial Intelligence, Machine Learning and Deep Learning, Data Science, Data Scientist
- Python, Keras, Colab, Pandas
- Linear Algebra, Calculus & Statistics for Machine Learning
- Regression, Classification, Nearest Neighbours, Naive Bayes, Logistic Regression, SVM, Random Forest, XGBoost, Gradient Boosting
- Neural Networks, Backpropagation, Computer Vision, Convolutional Neural Networks, Covnets, Finetuning, Data Augmentation …
Rating 3.8 out of 5 (25 ratings in Udemy)
What you'll learn- Artificial Intelligence, Machine Learning and Deep Learning, Data Science, Data Scientist
- Python, Keras, Colab, Pandas
- Linear Algebra, Calculus & Statistics for Machine Learning
- Regression, Classification, Nearest Neighbours, Naive Bayes, Logistic Regression, SVM, Random Forest, XGBoost, Gradient Boosting
- Neural Networks, Backpropagation, Computer Vision, Convolutional Neural Networks, Covnets, Finetuning, Data Augmentation
- Word Embeddings, Recurrent Neural Networks, LSTN, GRU, Transformer, Attention, Machine Translation
- Generative Deep Learning, Variational Auto-Encoders, GAN
DescriptionImagine being frustrated because you do not understand what AI, Machine Learning and Deep Learning are all about. Mastering the subjects of this course will give you access to a variety high paying jobs. This is the only course on the market that explains every little detail in a logical sequence. This course is no walk in the park and will require discipline and hard work.
No prior knowledge is required and the course offers math refreshers in linear algebra, calculus and statistics. The course assumes that you have no prior coding experience. All the software that we use is open source and free. We explain every block of code so that you are able to build up coding skills.
The course is delivered through simple whiteboard and screen recording sessions. The code is made available via .ipynb files attached to the lecture itself. Notes are available for the majority of the lectures, except for lectures 1 to 12 as these lectures are more descriptive. Reference is made to my Github account (mfavaits) where some of the notes can be found as well. The majority of the notes are handwritten. The notes by itself are a tough read but having them in front of you when looking at the videos will help you.
