Artificial Intelligence Course in Karachi
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Introduction
- Data Science Play Ground
- What is Machine Learning.
- First Image CLassifier.
- Data Science and Machine Learning Cheat Sheet
Data Science and Machine Learning
- Recommender Systemt using K nearst Means
- Data Science vs Machine Learning vs Artificial Intelligence
- Sumarizing it all
AI Project Life Cycle
- AI Project Framework
- STep-1 Problem Defination
- Step-2 Data
- Step-3 Evaluation.
- Step-4 Features
- Step-5 Modelling.
- Step-5 Data Validation
- Step-6 Course Correction
- Tools needed for AI Project
Python the Most Powerful Language
- What is Programming Language
- Python Interpreter and First Code
- Python 3 vs Python 2
- Formula to Learn Coding
- Data Types and Basic Arithmatic
- Basic Arithmetic Part-2
- Rule of Programming
- Mathematical Operators and Order of Precedence
- Variables and their BIG No No
- Statement vs Expression
- Augmented Assignment Operator
- String Data Type
- String Concatenation
- type Conversion
- String Formatting
- Indexing
- Immutability
- Built in Function and Methods
- Boolean Data Type
- Exercise
- Data Structor and Lists
- Lists continued
- Matrix from Lists
- List Methods
- Lists Methods 2
- creating Lists Programatically
- Dictionary
- Dic key is Un Changeable
- Most Used Methods on Dictionaries
- Tuple Data Types
- Sets data Types
- Intro to Process of Coding-Conditionals
- if else Statement
- AND OR keywords
- Boolean result of Different values
- Logical Operators
- Identity Operator
- for loop and Iterables
- Nested For loop
- Exercise for loop
- Range Function
- While Loop
- Continue Break Pass Keywords
- Exercise Draw a Shape
Python Part-2
- Functions
- Why of Functions
- Parameter vs Argument
- Default Parameters
- Return Keyword
- Doc String
- Good Programming Practices
- args and kwargs
- Exercise
- Scope of a Function
- Scope Rules-1
- Scope Rules-2
- GLobal vs nonlocal Keywords
- Programming Best Practices-2
- Special Functions map
- Special Functions Filter.
- Special Functions Zip
- Special Functions reduce
- List Comprehension Case-1,2 and 3
- Sets and Dictionary Comprehension
- Python Modules
- Python packages
Environment Setup for Machine Learning Projects
- Tools for Data Science Environment
- Who is Mr. Conda
- Setting Up Machine Learning Project
- Blue Print of Machine Learning Project
- Installing conda
- Installing tools
- Starting Jupyter Notebook
- Installing for MacOS and Linux
- Walkthrough of Jupyter notebook 1
- Walkthrough of Jupyternotebook 2
- Loading and Visualizing Data
- Summing it Up
Pandas for Data Analysis
- Tools needed
- Pandas and What we Will cover
- Data Frames
- How to Import Data
- Describing Data
- Data Selection
- Data Selection 2
- Changing Data
- Add Remove Data
- Manipulating Data
NumPy
- What and Why of Numpy
- Numpy Array
- Shape of Array
- Important Functions on Arrays
- Creating Numpy array
- random seed
- Accessing Elements
- Array Manipulation
- Aggregations
- mean variance and std
- Dot Product vs Matrix Manipulation
- Dot Product
- Reshape and Transpose
- Exercise
- Comparison Operators
- Sorting Arrays
- Reading Images
Matplotlib
- matplotlib Into
- First Plot with matplotlib
- Methods to Plot
- settingup Features
- One Figure Many Plots
- Most Used Plots Bar plot
- Histogram
- Four plot one figure
- Pandas Data Frame
- Plotting from Pandas Data Frame
- Bar plot from Pandas Data Frame
- pyplot vs OO methods
- Life Cycle of OO method
- Life Cycle of OO method Advanced
- Customization Part-2
- Customization Part-3
- Figure Styling
- Naming Entire Figure
Scikit-Learn
- What Actually ML Model is
- Intro to Sklearn
- Step-1 Getting Data Ready Split Data
- Step-2 Choosing ML model
- Step-3 Fit Model
- Step-4 Evaluate Model
- Step-5 Improve Model
- Step-6 Save Model.
- What we are going to Do
- Step-1 Getting Data Split Data
- Step-1 Getting Data Ready Converting Part-1
- Getting Data Ready Converting Part-2
- Getting Data Anatomy of Conversion
- Getting Data Second Method of Conversion
- Getting Data Missing Values.
- Getting Data Missing Values method 2
- Choosing Machine Learning Model
- Using map to choose model
- Step-2 How to Choose Better model
- Choosing Model for Classification problem
- Fit the Model
- Running Prediction
- Step-3 predict_proba method
- Step-3 Running Prediction on Regression Problem
- Step-4 Evaluating Machine Learning Model Default Scoring
- Step-4 WHat is Cross Validation
- Step-4 Accuracy (Classification Model)
- Step-4 Area Under the Curve Part-1
- Step-4 Area Under the Curve Part-2.
- Step-4 Area Under the Curve Part-3 Plotting
- Confusion Matrix Calculate
- Step-4 Confusion Matrix Plot
- Step-4 Classification Report Important concepts
- Step-4 Classification Report Fully Explained
- Step-4 R2 for Regression Problems
- Step-4 Mean Absolute Error for Regression Problems
- Step-4 Mean Square Error for Regression Problems
- Step-4 Scoring parameters for Classification
- Step-4 Scoring parameters for Regression
- Step-4 Evaluation using Functions Classification
- Step-4 Evaluation using Functions Regression
- Step-5 Improving Model by Hyper parameters
- Step-5 Improving Model by Hyperparameters manually
- Step-5 Hyperparameters Task-1
- Step-5 Evaluation Metrics in One Function
- Step-5 Hyperparameters Comparison
- Tunning Hyperparameters using RSCV
- Tunning Hyperparameters using RSCV Part-2
- Tunning Hyperparameters using GSCV
- Results Comparison
- Save Load Model with Pickle Method-1
- Save Load Model with joblib Method-2
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