What will you learn?
Master Python programming from fundamentals to advanced concepts with hands-on coding exercises in our Python Developer Bootcamp.
Build real-world applications using Python frameworks like Django and Flask through project-based learning in our Python Web Development Course.
Automate tasks and analyze data using Python libraries like Pandas, NumPy, and Matplotlib in our Python for Data Science Training.
Solve complex problems with Python algorithms and prepare for technical interviews with our Python Coding Interview Prep program.
Course Content for Python, Math/Statistics, and Machine Learning
- Installing Python and IDEs (VS Code, Jupyter Notebook)
- Writing your first Python script
- Understanding indentation and syntax
- Input and output operations
- Commenting code
- Declaring variables and data types
- Integer, float, and string operations
- String formatting and manipulation
- Type casting and type checking
- Creating and accessing lists
- List operations (append, remove, pop, slicing)
- Conditional statements (if, else, elif)
- Loops (for and while loops)
- Loop control (break, continue, pass)
- Defining and calling functions
- Arguments, return values, default parameters
- Dictionaries: creating, accessing, updating
- Tuples: immutable sequences
- Reading from and writing to files
- Introduction to object-oriented programming
- Creating classes and objects
- Constructors and methods
- Inheritance basics
- Try, except blocks for error handling
- Raising and creating custom exceptions
- Introduction to NumPy arrays
- Array operations and slicing
- Broadcasting concepts
- Mathematical functions on arrays
- Reading and writing arrays
- Loading datasets using Pandas
- DataFrame creation and manipulation
- Data cleaning basics (missing values, duplicates)
- Plotting graphs using Matplotlib
- Creating visualizations with Seaborn
- List comprehensions
- Dictionary comprehensions
- Set comprehensions
- Set operations (union, intersection, difference)
- Parsing and writing JSON data
- Introduction to iterators and generators
- Writing custom generators
- Understanding and creating decorators
- Understanding REST APIs
- Making API requests with requests library
- Parsing API responses (JSON/XML)
- Error handling in API calls
- Authenticating API requests (API keys, tokens)
- Setting up and configuring logging
- Writing and running unit tests with Pytest
- Validating data using Pydantic models
- Connecting Python to databases (using SQLite/MySQL)
- Executing CRUD operations
- Importing and exploring retail sales datasets
- Cleaning and preprocessing data
- Analyzing sales patterns and customer behavior
- Visualizing insights through graphs and charts
- Summarizing findings and preparing a report
- Overview of course goals and structure
- Importance of math and statistics in data science
- Real-world applications of statistical thinking
- Types of data: qualitative vs. quantitative
- Data collection and data cleaning basics
- Introduction to data visualization tools (Matplotlib, Seaborn)
- Building basic charts: bar plots, histograms, scatter plots
- Mean, median, and mode: definitions and differences
- Range, variance, and standard deviation
- Interquartile range (IQR) and boxplots
- How to interpret central tendency and variability
- Fundamentals of probability
- Independent vs. dependent events
- Conditional probability and Bayes' Theorem
- Real-world probability examples (e.g., customer churn)
- Normal distribution and its properties
- Binomial distribution
- Poisson distribution
- Understanding skewness and kurtosis
- Customer segmentation using data
- Identifying potential customer groups
- Using descriptive statistics to profile target segments
- Visualization of market demographics
- Concept and significance of CLT
- Sampling distributions explained
- Law of large numbers
- Practical implications in data analysis
- Null and alternative hypotheses
- Type I and Type II errors
- P-values and significance levels
- Conducting t-tests and z-tests
- Setting up an A/B test
- Defining control and treatment groups
- Measuring test success with metrics
- Analyzing A/B test results and drawing conclusion
- What is Machine Learning?
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- ML vs Traditional Programming
- Key concepts: Features, Labels, Models, Training, Prediction
- Introduction to popular ML libraries (Scikit-learn, TensorFlow)
- Understanding regression problems
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Regularization techniques: Ridge, Lasso, ElasticNet
- Evaluation metrics: MAE, MSE, RMSE, R² score
- Understanding classification problems
- Logistic Regression
- Decision Trees
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Naive Bayes
- Evaluation metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix
- Concept of ensemble methods
- Bagging: Random Forest
- Boosting: AdaBoost, Gradient Boosting, XGBoost
- Stacking and Voting Classifiers
- Advantages and disadvantages of ensemble methods
- Train/Test Split and Cross-Validation
- Bias-Variance Tradeoff
- Hyperparameter tuning (Grid Search, Random Search)
- Overfitting and Underfitting
- ROC Curve and AUC Score
- Problem definition
- Data collection and exploration
- Data preprocessing and cleaning
- Feature engineering and selection
- Model building and evaluation
- Model deployment and monitoring
- Handling missing data
- Encoding categorical variables
- Feature scaling: normalization and standardization
- Feature selection techniques
- Creating new features from existing data
- What is Unsupervised Learning?
- Clustering: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Applications of clustering and reduction techniques
- Evaluating unsupervised models (silhouette score, etc.)
- Understanding the housing dataset
- Exploratory Data Analysis (EDA)
- Data cleaning and preprocessing
- Building and evaluating a regression model
- Interpreting the results and drawing business insight
- Introduction to MLOps concepts
- Model versioning and reproducibility
- Deployment strategies (batch vs real-time predictions)
- Using cloud platforms: AWS SageMaker, Azure ML, GCP AI Platform
- Monitoring and retraining models in production
Requirements
Everything You Need to Get Started:A Passion for Coding – Bring your enthusiasm for problem-solving and creating with code!
Basic Computer Skills – Familiarity with operating a computer (installing software, file management) is all you need to begin.
Logical Thinking – Python programming rewards clear, structured thinking more than advanced math skills.
Growth Mindset – Be ready to experiment, make mistakes, and learn from them - that's how programmers grow!
Consistent Practice – Just 30 minutes daily of coding practice will yield amazing results over time.
Meet your instructor
Mr. Hemant Sethi
AWS Certified Solutions Architect | 20+ Years of ExpertiseHemant, an AWS Certified Solutions Architect, specializes in designing scalable systems and seamless migrations. Passionate about training, he simplifies AWS concepts, empowering professionals to master cloud technologies and achieve success.
Buy for 12% off
This course include:
55+ Hours of Live Interactive Classes for in-depth learning.
Hands-On Experience with a Real-Time Project to apply your skills.
100% Personalized Doubt-Solving Support for tailored assistance.
Expert guidance to crack interviews and land your dream job at top global companies.
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