Complete Machine Learning & Data Science - Skill Up
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Complete Machine Learning & Data Science Program - With Python, Deep Learning is a structured, hands-on program designed to help learners build a solid foundation and expertise in the data science field. Covering everything from programming in Python to advanced deep learning techniques and reinforcement learning, this course provides end-to-end knowledge for aspiring data science students.
Serious & committed learners can still earn up to 90% of their course fee back by taking the Three 90 Challenge.
Course Overview
Start your journey in data science and machine learning with the GeeksforGeeks Skill Up Program for Complete Machine Learning & Data Science. This 16-week intensive course combines theory, coding, and hands-on practice to take you through all critical areas of modern data science.
The course kicks off with a solid foundation in Python, statistics, and exploratory data analysis, then builds up to machine learning, deep learning, and model deployment. Also gain experience with libraries like Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch, and more.
From building predictive models to deploying them using Streamlit and Flask, this course offers everything needed to become a confident and capable data scientist.
The Data Science course kicks off with a solid foundation in Python, statistics, and exploratory data analysis, then builds up to machine learning, deep learning, and model deployment. Also gain experience with libraries like Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch, and more.
From building predictive models to deploying them using Streamlit and Flask, this course offers everything needed to become a confident and capable data scientist.
Complete Machine Learning & Data Science - Highlights
- Master Python programming from scratch
- Dive deep into statistics and probability for data science
- Perform exploratory data analysis using Pandas and NumPy
- Learn data visualization using Matplotlib, Seaborn, and Plotly
- Build, evaluate, deploy and optimize machine learning models
- Hands-on projects using real-world datasets
- Advanced deep learning with CNN, RNN, Transformers, GANs
- Introduction to Reinforcement Learning and real-world use cases
- Deploy ML models using Streamlit and Flask
Course Content
- What is Data Science and its Overview
- Installing Python and setting up the environment
- Input/Output, Variables, Keywords
- Data Types, Operators, Conditional Statements
- Loops and Functions
- Strings, Lists, Dictionaries, Tuples, Sets
- Python Collections and Comprehensions
- Error Handling, File Handling, Advanced Python (Generators, Decorators)
- Descriptive Statistics, Bayes' Theorem
- Covariance, Correlation, Distributions (Normal, Binomial, Poisson)
- Inferential Statistics: Hypothesis Testing
- Z-test, T-test, Chi-Square, ANOVA
- Confidence Intervals, A/B Testing, MANOVA
- Feature selection with ANOVA, Chi-Square
- NumPy for numerical analysis
- Data Analysis with Pandas
- Handling Missing & Duplicate Data
- Groupby Operations, Outlier Handling
- Correlation Matrix, Time Series Visualization
- Hands-on Contest Problems with Pandas and NumPy
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