This training consits of 2 Levels 10 sessions per level:

1. Introductory level (Python, Data manipulation and Machine Learning Basics).
2. Advanced Machine Learning (Neural networks, CNN & RNN).

And it is under the supervision of Dr.Wafaa Rady, head of Communication and Electronics Engineering at the Canadian International College.

• Introduction.
• Input, Processing, and output.
• Decision Structures and Boolean Logic.

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• Repetition structures.
• Functions and Modules.
• Files and Exceptions.

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• Lists and Tuples.
• Strings.
• Dictionaries and Sets.

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• Classes and Object-Oriented Programming.
• Inheritance/ Polymorphism.
• Recursion.

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• All about Numpy.
• NumPy vs Lists (speed, functionality).
• Applications of NumPy.
• The Basics (creating arrays, shape, size, data type).
• Accessing/Changing Specific Elements, Rows, Columns, etc (slicing).
• Initializing Different Arrays (1s, 0s, full, random, etc...).
• Basic Mathematics (arithmetic, trigonometry, etc.).
• Reorganizing Arrays (reshape, vstack, hstack).

• Raw data to Features.
• All about pandas.
• Loading the data into Pandas.
• Iterate through each Row.
• Getting rows based on a specific condition.
• High Level description of your data (min, max, mean, std dev, etc.).
• Sorting Values (Alphabetically, Numerically).
• Making Changes to the DataFrame.
• Adding/ Deleting columns.
• Summing Multiple Columns to Create new Columns.
• Rearranging columns
• Saving our Data (CSV, Excel, TXT, etc.).
• Filtering Data (based on multiple conditions).
• Reset Index.
• Regex Filtering (filter based on textual patterns).
• Conditional Changes.
• Aggregate Statistics using Groupby (Sum, Mean, Counting).
• Working with large amounts of data (setting chunksize).

• Why Data Visualization.
• What Is Data Visualization).
• All about Matplotlib.
• Various Types Of Plots.
  • Line Graph.
  • Histogram.
  • Pie Chart.
  • Box & Whisker Plot.

• Why Machine Learning.
• What is Machine Learning.
• Types of Machine Learning.
• Supervised Learning.
• Reinforcement Learning.
• Supervised VS Unsupervised.
• Classification.

• Linear Regression.
• Application of Linear Regression.
• Regression Equation.
• Multiple Linear Regression.
• Logistic Regression.
• Comparing Linear & Logistic Regression.
• K-Means Clustering.
• K-Nearest Neighbors.

• Decision Tree.
• Random Forest.
• Support Vector Machine.
• Naive Bayes.

1. Certificate that can be used for field training 1 or field training 2.
2. Strong profile on Github.
3. Department award for top 3 students.
4. Students will be certified with a total of 140 hrs hands-on experience.
5. Daily office hours for support/guidance.
6. Get the chance to meet new students who share your interests.
7. Start exploring and building models on Kaggle which is the largest online community for data scientists and machine learning practitioners.

1. Students must attend +80% of total sessions.
2. Students must hand in all the required projects within 48 hrs after each session using an online platform (Github)

1. Each session will be uploaded in a private playlist on youtube for ease of access after the session.
2. Students with a github account have privilege during the selection process.
3. Required projects after each session could be adjusted.
4. Lack of commitment could lead to exclusion.
5. Students with basics of any programming language are preferable.
6. Additional sessions may be set to fulfill the content.
7. Student may need to bring his/her personal laptops.

1. Starting out with python - Third Edition - Tony Gaddis
2. Hands-on Machine Learning with Scikit-Learn & TensorFlow - Aurélien Géron