Machine-Learning-Training

Training Description:

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.

Assignments Form: here

Facebook Group: here

Whatsapp Group: here

Report Problem: here

Level 1 Training Content:

Python:

Session 1:

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

Find all solved examples in the lecture here
Find the assignment
Check out the slides
Check out the Recorded Lecture for session 1
Check out the Recorded Lab
Check out the Solution of the assignment
Once you submitted Assignment 1 Fill the form here Deadline: Monday, April 19 at 11:00 PM

Session 2:

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

Find all solved examples in the lecture here
Find assignment 2 - part 1 or here
Find assignment 2 - part 2 or here
Check out the slides
Check out the Recorded Lecture for session 2- Part 1
Check out the Recorded Lecture for session 2- Part 2
Check out the Recorded Lab for session 2
Check out the Recorded Lab for Session 2.3 + Lab
Check out the Solution of assignment 2
Check out the Solution of assignment 2 - part 2
Once you submitted Assignment 2 Fill the form here Deadline: Saturday, May 1 at 11:55PM
Once you submitted Assignment 2/ part 2 Fill the form here Deadline: Saturday, May 15 at 11:55PM

Session 3:

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

Find all solved examples in the lecture here
Find the assignment assignment or here
Check out the Recorded Lecture for session 3- Part 1 - Lists and Tuples
Check out the Recorded Lecture for session 3- Part 2 - Strings
Check out the Recorded Lecture for session 3- Part 3 - Dictionaries and Sets
Check out the Solution of assignment 3 or here
Check out the Recorded Lab for session 3
Once you submitted Assignment 3 Fill the form hereDeadline: 6 of july 11:55 pm

Session 4:

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

Find all solved examples in the lecture here
Find the assignment assignment or here
Check out the slides
Check out the Solution of assignment 4 or here
Once you submitted Assignment 4 Fill the form hereDeadline: 15 of july 11:55 pm

Feature Engineering:

Session 5:

• 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).

Numpy Lecture or here
Find the assignment assignment or here

Session 6:

• 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).

Pandas Lecture or here
Find the assignment assignment or here

Session 7:

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

Matplotlib Lecture or here
Find the assignment assignment or here

Machine Learning:

Session 8:

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

Session 9:

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

Session 10:

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

Final Project:

Introduction:

For the final project, you will conduct your own data analysis and create a file to share that documents your findings. You should start by taking a look at your dataset and brainstorming what questions you could answer using it. Then you should use pandas and NumPy to answer the questions you are most interested in, and create a report sharing the answers. You will not be required to use inferential statistics or machine learning to complete this project, but you should make it clear in your communications that your findings are tentative. This project is open-ended in that we are not looking for one right answer.

Step One - Choose Your Data Set

Click this link (available in a Google doc here) to open a document with links and information about data sets that you can investigate for this project. You must choose one of these datasets to complete the project.

Step Two - Get Organized

Eventually you’ll want to submit your project (and share it with friends, family, and employers). Get organized before you begin. We recommend creating a single folder that will eventually contain:

• The report communicating your findings
• Any Python code you wrote as part of your analysis
• The data set you used (which you will not need to submit)
  

You may wish to use a Jupyter notebook, in which case you can submit both the code you wrote and the report of your findings in the same document. Otherwise, you will need to submit your report and code separately. If you would like a notebook template to help organize your investigation, you can click here.

Step Three - Analyze Your Data

Brainstorm some questions you could answer using the data set you chose, then start answering those questions. You can find some questions in the data set options to help you get started.Try and suggest questions that promote looking at relationships between multiple variables. You should aim to analyze at least one dependent variable and three independent variables in your investigation. Make sure you use NumPy and pandas where they are appropriate!

Step Four - Share Your Findings

Once you have finished analyzing the data, create a report that shares the findings you found most interesting. If you use a Jupyter notebook, share your findings alongside the code you used to perform the analysis. Make sure that your report text is contained in Markdown cells to clearly distinguish your comments and findings from your code work. You should also feel free to use other tools and software to craft your final report, but make sure that you can submit your report as an HTML or PDF file so that it can be opened easily.

Step Five - Review

Push to your github repo, it will be reviewed and feedback will be sent to you shortly.

Benefits:

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

Evaluation Method to receive certificates:

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)

Notes:

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.

References:

1. Starting out with python - Third Edition - Tony Gaddis
2. Python Data Science Handbook Essential Tools for Working with Data
3. Hands-on Machine Learning with Scikit-Learn & TensorFlow - Aurélien Géron