Course Plan :
Module 1 : Intuition to coding and Python.
Module 2 : Required math.
Module 3 : Data visualization packages (covers data science)
NumPy, Pandas, Matplotlib, Scikit-Learn
Module 4a : Intuition behind Machine learning. (Scikit-Learn)
Module 4b : Machine learning hands-on (Sci-kit learn)
Module 5 a : Intuition behind Artificial intelligence
Module 5b : Artificial intelligence hands on. (Tensor flow and keras)
Module 6* : Project, Git-hub, Kaggle.
Recommended Books:
1. Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners Paperback – Illustrated, November 12, 2019
by Al Sweigart
2. Python Data Science Handbook
by Jake VanderPlas
Released November 2016, Publisher(s): O'Reilly Media, Inc.
ISBN: 9781491912058
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
by Aurélien Géron. Released September 2019, Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492032649
4. Deep Learning with Python 1st Edition
by François Chollet (Author), Manning Publications; 1st edition (December 22, 2017)
ISBN-10 : 9781617294433
Exam syllabus
Following topics from the Cambridge IGCSE and Secondary checkpoint level Mathematics:
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Algebra
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Sets and Venn diagrams
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correct ordering of operations (BIDMAS) and use of brackets
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Money
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Rules of indices
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Basic statistics (questions based on measures of central tendency and variation)
Following topics from Logical Reasoning:
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Alphabet sequence
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Series
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Arrangement (seating rank)
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Problem Solving (Analytical)
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Pictorial Reasoning
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Analogy.
You can find some sample material for the topics mentioned above by clicking here.