Artificial Intelligence Course (Machine Learning)
Artificial Intelligence Course (Machine Learning)
Free online lecture introducing our Artificial Intelligence course.
Book your seat now!
By the end of the course, students will be able to go through the Machine Learning lifecycle. They will also be able to decide upon the data pre-processing needed according to the data available and choose the algorithm that is suitable for the application.
Duration:
The workshop spans over 10 weeks. Participants are expected to attend 3 sessions, 2 hours per session on a weekly basis, and make practice exercises at home during the week.
- Date: from Nov 1, 2021 to Jan 8, 2022 (10 Weeks)
- Days: Saturday, Monday, Wednesday
- Time: 7:00 PM - 9:00 PM ( Beirut Time)
Program:
- Python programming
-
- Python101
- Variables
- Constant
- DataTypes
- Operators
- Loops
- Functions
- Python Code & Modules
- Conditional statements
- PIP, IDE, Virtual Env
- Compiler VS Interpreter
2. Machine Learning
-
- Introduction TO Machine Learning
- Introduction to Artificial Intelligence
- Difference between AI,ML&DL
- Difference Between Information And Data
- List & Arrays
- Enumerate()
- Python Exception Handling
- Recursion in Python
- numpy.where() in Python
- Pandas dataframe.groupby()
- Loc, iloc,dataframe
- Stackoverflow
- Git & GitHub
- Python Lambda Functions
- format() function
- Linear Regression
- Gradient Descent in Linear Regression
- Linear Regression in Python with Scikit-Learn
- Multiple Linear Regression using Python
- Introduction to Logistic Regression
- (Scikit-Learn): Logistic Regression Classification
- Multiclass classification using scikit-learn
- Supervised and Unsupervised learning
- random state in sklearn train_test_split
- model-fit-vs-model-predict
- ML | Cost function in Logistic Regression
- Decision Tree Classification in Python
- Underfitting and Overfitting
- Categorical encoding using Label-Encoding and One-Hot-Encoder
- support-vector-machine
- Error Types
- KNN Algorithm
- Hybid System
- Neural Networks
- Neural Networks Implementation
Materials
- Students will have references for each topic.
- Students will have access to the exercise files as well as the data used to conduct the exercises.
- Zoom platform for online sessions.