Course Brief
About this Course
This course covers the fundamentals of artificial intelligence, machine learning, and data science. You will learn key concepts, techniques, and tools used in AI and data science to analyze and manipulate data, build predictive models, and apply machine learning algorithms.
Learn the fundamentals of AI, machine learning, and data science for beginners.
Eligibility
Requirements
- Basic understanding of mathematics and statistics.
- A computer with internet access.
- Willingness to learn machine learning algorithms and data science techniques.
Training Plan
Learning Roadmap
01
Foundation Studio: What is YouTube
- What is Machine Learning
- Data Science Play Ground
- First Image Classifier
- guided portfolio activity Exercise-01
02
Practical Lab: Data Science and Machine Learning
- Practice recommender system through a guided ai and data practice activity.
- Data Science vs Machine Learning vs Artificial Intelligence
- Practice summarizing it all through a guided ai and data practice activity.
- guided portfolio activity Exercise- 02
03
Applied Workflow: AI Project Life Cycle
- AI Project Framework
- Step-1 Problem Definition
- Practice step-2 data through a guided ai and data practice activity.
- Practice step-3 evaluation through a guided ai and data practice activity.
- Practice step-4 features through a guided ai and data practice activity.
- Practice step-5 modelling through a guided ai and data practice activity.
- Step-5 Data Validation
- Step-6 Course Correction
- Tools needed for AI Project
- guided portfolio activity Exercise- 03
04
Professional Practice: 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 Arithmetic
- Rule of Programming
- Mathematical Operators and Order of Precedence
- guided portfolio activity Exercise- 04
05
Portfolio Sprint: Python the Most Powerful Language Part 02
- Variables and their BIG No No
- Statement vs Expression
- Augmented Assignment Operator
- Practice string data type through a guided ai and data practice activity.
- String Concatenation
- Practice type conversion through a guided ai and data practice activity.
- Practice string formatting through a guided ai and data practice activity.
- Practice indexing through a guided ai and data practice activity.
- Practice immutability through a guided ai and data practice activity.
- guided portfolio activity Exercise- 05
06
Career Readiness: Python the Most Powerful Language Part 03
- Built in Function and Methods
- Practice boolean data type through a guided ai and data practice activity.
- Practice exercise through a guided ai and data practice activity.
- Data Structure and Lists
- Practice lists continued through a guided ai and data practice activity.
- Practice matrix from lists through a guided ai and data practice activity.
- Practice list methods through a guided ai and data practice activity.
- Practice lists methods 2 through a guided ai and data practice activity.
- Creating Lists Programmatically
- guided portfolio activity Exercise- 06
07
Foundation Studio: Python the Most Powerful Language Part 04
- Practice dictionary through a guided ai and data practice activity.
- Dic Key is Un Changeable
- Most Used Methods on Dictionaries
- Practice tuple data types through a guided ai and data practice activity.
- Practice sets data types through a guided ai and data practice activity.
- Intro to Process of Coding Conditionals
- Practice if else statement through a guided ai and data practice activity.
- Practice and or keywords through a guided ai and data practice activity.
- Boolean result of Different values
- Practice logical operators through a guided ai and data practice activity.
- guided portfolio activity Exercise- 07
08
Practical Lab: Python the Most Powerful Language Part 05
- Practice identity operator through a guided ai and data practice activity.
- for loop and Iterables
- Practice nested for loop through a guided ai and data practice activity.
- Practice exercise for loop through a guided ai and data practice activity.
- Practice range function through a guided ai and data practice activity.
- Practice while loop through a guided ai and data practice activity.
- Continue Break Pass Keywords
- Exercise Draw a Shape
- guided portfolio activity Exercise- 08
09
Applied Workflow: Python Part-2
- Practice functions through a guided ai and data practice activity.
- Practice why of functions through a guided ai and data practice activity.
- Parameter vs Argument
- Practice default parameters through a guided ai and data practice activity.
- Practice return keyword through a guided ai and data practice activity.
- Practice doc string through a guided ai and data practice activity.
- Good Programming Practices
- Practice args and kwargs through a guided ai and data practice activity.
- Practice exercise through a guided ai and data practice activity.
- Scope of a Function
- Practice scope rules-1 through a guided ai and data practice activity.
- Practice scope rules-2 through a guided ai and data practice activity.
- guided portfolio activity Exercise- 09
10
Professional Practice: Python Part-3
- Global vs nonlocal Keywords
- Programming Best Practices-2
- Special Functions map
- Special Functions Filter
- Special Functions Zip
- Special Functions reduce
- List Comprehension Case-12 and 3
- Sets and Dictionary Comprehension
- Practice python modules through a guided ai and data practice activity.
- Practice python packages through a guided ai and data practice activity.
- guided portfolio activity Exercise- 10
11
Portfolio Sprint: Environment Setup for Machine Learning Projects
- Practice who is mr. conda through a guided ai and data practice activity.
- Tools for Data Science Environment
- Setting Up Machine Learning Project
- Blueprint of Machine Learning Project
- Practice installing conda through a guided ai and data practice activity.
- Practice installing tools through a guided ai and data practice activity.
- Starting Jupyter Notebook
- Installing for MacOS and Linux
- Walkthrough of Jupyter notebook 1
- Walkthrough of Jupyter notebook 2
- Loading and Visualizing Data
- Practice summing it up through a guided ai and data practice activity.
- guided portfolio activity Exercise- 11
12
Career Readiness: Pandas for Data Analysis
- Practice tools needed through a guided ai and data practice activity.
- Pandas and What we Will cover
- Practice data frames through a guided ai and data practice activity.
- Practice how to import data through a guided ai and data practice activity.
- Practice describing data through a guided ai and data practice activity.
- Practice data selection through a guided ai and data practice activity.
- Practice data selection 2 through a guided ai and data practice activity.
- Practice changing data through a guided ai and data practice activity.
- Practice add remove data through a guided ai and data practice activity.
- Practice manipulating data through a guided ai and data practice activity.
- guided portfolio activity Exercise- 12
13
Foundation Studio: NumPy
- What and Why of Numpy
- Practice numpy array through a guided ai and data practice activity.
- Practice shape of array through a guided ai and data practice activity.
- Important Functions on Arrays
- Creating Numpy array
- Practice random seed through a guided ai and data practice activity.
- Practice accessing elements through a guided ai and data practice activity.
- Practice array manipulation through a guided ai and data practice activity.
- Practice aggregations through a guided ai and data practice activity.
- guided portfolio activity Exercise- 13
14
Practical Lab: NumPy Part 02
- mean variance and std
- Dot Product vs Matrix Manipulation
- Practice dot product through a guided ai and data practice activity.
- Reshape and Transpose
- Practice exercise through a guided ai and data practice activity.
- Comparison Operators
- Practice sorting arrays through a guided ai and data practice activity.
- Practice reading images through a guided ai and data practice activity.
- guided portfolio activity Exercise- 14
15
Applied Workflow: Matplotlib
- Practice matplotlib intro through a guided ai and data practice activity.
- First Plot with matplotlib
- Practice methods to plot through a guided ai and data practice activity.
- Setting up Features
- One Figure Many Plots
- Most Used Plots Bar plot
- Practice histogram through a guided ai and data practice activity.
- Four plot one figure
- Practice pandas data frame through a guided ai and data practice activity.
- guided portfolio activity Exercise- 14
- Plotting from Pandas Data Frame
16
Professional Practice: Matplotlib Part 02
- 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
- Practice figure styling through a guided ai and data practice activity.
- Naming Entire Figure
- guided portfolio activity Exercise- 15
17
Portfolio Sprint: Scikit-Learn
- What Actually ML Model is
- Practice intro to sklearn through a guided ai and data practice activity.
- Step-1 Getting Data Ready Split Data
- Step-2 Choosing ML model
- Practice step-3 fit model through a guided ai and data practice activity.
- Step-4 Evaluate Model
- Step-5 Improve Model
- Practice step-6 save model through a guided ai and data practice activity.
- guided portfolio activity Exercise- 16
18
Career Readiness: Scikit-Learn Part 02
- 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
- guided portfolio activity Exercise- 17
19
Foundation Studio: Scikit-Learn Part 03
- Choosing Model for Classification problem
- Practice fit the model through a guided ai and data practice activity.
- Practice running prediction through a guided ai and data practice activity.
- 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
- guided portfolio activity Exercise- 18
20
Practical Lab: Scikit-Learn Part 04
- 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
- guided portfolio activity Exercise- 19
21
Applied Workflow: Scikit-Learn Part 05
- Step-5 Improving Model by Hyperparameters
- Step-5 Improving Model by Hyperparameters manually
- Step-5 Hyperparameters Task-1
- Step-5 Evaluation Metrics in One Function
- Step-5 Hyperparameters Comparison
- Tuning Hyperparameters using RSCV
- Tuning Hyperparameters using RSCV Part-2
- Tuning Hyperparameters using GSCV
- Practice results comparison through a guided ai and data practice activity.
- guided portfolio activity Exercise- 20
22
Professional Practice: Scikit Learn Part 06
- Save Load Model with Pickle Method-1
- Save Load Model with joblib Method-2
- Building Entire Model using Pipeline Part-1
- Building Entire Model using Pipeline Part-2
- Building Entire Model using Pipeline Part-3
- Building Entire Model using Pipeline Part-4
- guided portfolio activity Exercise- 21
23
Portfolio Sprint: Project-1 Part 01
- Milestone Project-1 Intro
- Creating Project Environment
- Practice first 4 steps through a guided ai and data practice activity.
- Data Features Recognition
- Importing Tools and Libraries
- Exploratory Data Analysis Part-1
- Exploratory Data Analysis Part-2
- guided portfolio activity Exercise- 22
24
Career Readiness: Project-1 Part 02
- Plotting Correlation Matrix Part-2
- Modelling Split the data
- Choosing the Right Model
- Practice improving model through a guided ai and data practice activity.
- Plotting the Improved Model Score
- Hyperparameter Tuning using GSCV
- Hyperparameters for RandomForestClassifier
- Running the model with Hyperparameters using GSCV
- Score Comparison after tuning
- guided portfolio activity Exercise- 24
25
Foundation Studio: Project-1 Part 03
- Hyperparameters Tuning Using Grid Search CV
- Practice summarizing through a guided ai and data practice activity.
- What have we learnt
- Area under the curve and Confusion Matrix
- Plot the Classification report
- Let's see if Cross Validation layers help us
- Visualizing Cross Validation Score
- Features Improvement
- Practice conclusion through a guided ai and data practice activity.
- guided portfolio activity Exercise- 23
26
Portfolio Sprint: AI and Data Practice Review
- Review the major ai and data practice concepts through a guided recap.
- Organize class practice into a simple portfolio-ready workflow.
- Apply the learned process on a realistic task with instructor guidance.
- Prepare next-step notes for continued practice after course completion.