AI ML DL with Python
Python is the undisputed standard language for Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) due to its highly readable syntax and powerful ecosystem of open-source libraries.
Machine Learning
- Machine Learning Fundamentals
- What Is Machine Learning?
- AI / Machine Learning / Data Science
- Machine Learning Playground
- YouTube Recommendation Engine
- Types of Machine Learning
- Machine Learning — Round 2
- Machine Learning Framework
- 6-Step Machine Learning Framework
- Types of Machine Learning Problems
- Types of Data
- Types of Evaluation
- Features in Data
- Splitting Data
- Picking the Model
- Model Tuning
- Model Comparison
- Overfitting and Underfitting
- Experimentation
- Tools Used
- Elements of AI
- Python/Conda/Jupyter Environment
- Introduction to Conda
- Conda environments
- Windows environment setup
- Linux environment setup
- Mac environment setup
- Sharing Conda environments
- Jupyter Notebook
- Downloading workbooks and assignments
- 3. Pandas
- Pandas introduction
- Series
- DataFrames
- CSV files
- Reading data from URLs
- Describing data
- Selecting and viewing data
- Manipulating data
- Assignments
- 4. NumPy
- NumPy introduction
- NumPy data types and attributes
- Creating arrays
- Random seed
- Arrays and matrices
- Array manipulation
- Standard deviation
- Variance
- Reshape
- Transpose
- Dot product
- Element-wise operations
- Comparison operators
- Sorting arrays
- Images as NumPy arrays
- NumPy practice assignment
- Matplotlib & Visualization
- Matplotlib introduction
- Importing and using Matplotlib
- Anatomy of a Matplotlib figure
- Scatter plots
- Bar plots
- Histograms
- Subplots
- Visualization tips
- Plotting Pandas DataFrames
- Regular expressions
- Customizing plots
- Saving and sharing plots
- 6. Scikit-learn & Machine Learning Workflow
- Scikit-learn introduction
- ML refresher
- Scikit-learn cheatsheet
- Typical Scikit-learn workflow
- Train/test splitting
- Data cleaning
- Data transformation
- Data reduction
- Converting data to numbers
- Missing-value handling
- Feature scaling
- Choosing the right model
- Regression
- Classification
- Decision trees
- Fitting models
- Making predictions
-
predict()vspredict_proba() - 7. Model Evaluation
- Cross-validation
- Accuracy
- ROC curve
- ROC-AUC
- Confusion matrix
- Classification report
- R²
- MAE
- MSE
- Model evaluation
- Cross-validation with scoring parameters
- Scikit-learn evaluation functions
- Correlation analysis
- 8. Model Improvement
- Improving ML models
- Hyperparameter tuning
- Metric comparison
- Saving and loading models
- Complete ML workflow
- Scikit-learn practice
- 9. Machine Learning Projects
- Project environment setup
- Framework setup
- Data exploration
- Finding patterns
- Data preparation
- Choosing models
- Experimentation
- Model tuning
- Hyperparameter tuning
- Model evaluation
- Feature importance
- Feature engineering
- Numerical missing values
- Categorical missing values
- RandomizedSearchCV
- Preprocessing
- Making predictions
Deep Learning
- Deep Learning with TensorFlow
- Google Colab
- GPU usage
- TensorFlow 2
- Loading data labels
- Image preparation
- Converting labels into numbers
- Validation sets
- Image preprocessing
- Batching
- Data visualization
- Inputs and outputs
- How machines learn
- Building a deep learning model
- Model summary
- Model evaluation
- Overfitting prevention
- Training neural networks
- TensorBoard
- Making predictions
- Transforming predictions to text
- Visualizing predictions
- Saving/loading trained models
- Training on full dataset
- Test-image predictions
- Kaggle submission
- Predictions on custom images
- Completing Dog Vision project
Python
- Python Intro
- Python Get Started
- Python Syntax
- Python Comments
- Python Variables
- Python Data Types
- Python Numbers
- Python Casting
- Modify Strings
- Concatenate Strings
- Escape Characters
- String Methods
- String Exercises
- Python Booleans
- Python Operators
- Python Lists
- Python Tuples
- Python Sets
- Python Dictionaries
- Input Functions
- Python If…Else
- if elif
- Python While Loops
- Nested While
- Coffee machine project
- Python For Loops
- Patterns program
- For else
- Arrays
- Implicit conversion
- Explicit Conversion
- Numpy
- Deep Copy
- Shallow Copy
- Different Arrays in Numpy
- Multi-Dimensional Array
- Conversion 1D array to multi D array
- Array Multiplication
- Matrix Multiplication
- Replacing the values in the array
- Replacing the values in the matrix
- User Defined Functions
- Types of Arguments
- Kwargs
- Scope of a variable
- Fibonacci
- Python
- Even and Odd count
- Factorial of a number
- Recursion
- Factorial using recursion
- Lambda functions or Lambda
- expression
- filter
- map
- reduce
- Decorators
- Functions
- Special Variable Name
- OOPS
- Constructors
- Class or Static Variables
- Instance variables
- Class Methods
- Instance Methods
- Accessors
- Mutators
- Static Methods
- Namespace
- Nested Class
- Inheritance
- Single Level
- Multilevel
- Multiple
- Method Resolution Order
- Polymorphism
- Duck Typing
- Operator Overloading
- Method Overloading
- Method Over Riding
- Abstraction
- Iterators
- Generators
- Errors
- Handling Exceptions
- finally
FAQs
Frequently asked questions
Some of the most common questions.
Where are AI, ML & Deep Learning used?
AI, ML, and Deep Learning are used in many real-world industries such as healthcare, banking, finance, e-commerce, manufacturing, automobiles, cybersecurity, agriculture, and real estate. Applications include recommendation systems, fraud detection, chatbots, computer vision, speech recognition, predictive analytics, autonomous systems, and Generative AI.
Why should I learn AI, ML & Deep Learning?
Learning AI, ML, and Deep Learning helps you build systems that can learn from data, make predictions, automate tasks, and solve complex business problems. These skills also complement Python, Data Science, Cloud, and Generative AI, making them useful for students and professionals moving toward AI-focused careers.
What is the job market like for AI/ML/DL professionals?
The AI job market includes roles such as AI/ML Engineer, Machine Learning Engineer, Data Scientist, Deep Learning Engineer, NLP Engineer, Computer Vision Engineer, AI Developer, and Generative AI Engineer. Demand varies by industry and experience, and employers commonly look for a combination of Python, mathematics/statistics, ML algorithms, Deep Learning, SQL, cloud platforms, APIs, and project experience.
What salary can I expect after learning AI/ML/DL?
Salary depends significantly on experience, skills, location, company, job role, and project expertise. In India, entry-level AI/ML roles can start around ₹4–8 LPA, while professionals with strong practical skills and experience can move into ₹10–20+ LPA roles.