Bootcamp Course Ages 17–46 Online 24 hr Certificate
Complete course on AI, Machine Learning & Deep Learning from Zero to Hero (Part 1)
This course provides advanced concepts of artificial intelligence. Students will learn in-demand skills such as machine learning, deep learning, NLP, generative AI, and many more.
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Sample build · Class 04
This course provides advanced concepts of artificial intelligence. Students will learn in-demand skills such as machine learning, deep learning, NLP, generative AI, and many more. Students will learn to approach problems in a systematic, data-driven way, which can be applied to various real-world challenges beyond just AI. Studying AI and ML can deepen your understanding of human intelligence and cognition, providing insights into how we learn, think, and make decisions.
What you'll learn
- Introduction to AI
- Introduction to Jupyter Notebook
- NumPy, Pandas. Matplotlib, Scikit-learn, Seaborn
- Supervised & Unsupervised Machine Learning
- Linear Regression, Linear regression in Multiple Variable
- Gradient Descent
- Dummy variable and One hot Encoding
- Training and Testing data
- Classification Algorithm in ML
- Logistic Regression(Binary & Multiclass Classification)
- KNN Classification
- Support Vector Machine
- Decision Tree Algorithm
- Naive Bayes classifier
- Random Forest Algorithm
- K-Fold Cross Validation
What you get
- Introduction to AI
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Classification Algorithm
- Regression Algorithm
- Clustering Algorithm
Prerequisites
- Previous knowledge of Python Advanced Course
Skills you'll build
Additional ProjectsConceptual SkillsCreativity & InnovationLogical ThinkingPerseverancePresentation & Communication SkillsProblem Solving
Curriculum
9 modules. 24 hours.
01 Introduction
- Introduction to AI
- Introduction to Machine Learning
- Introduction to Visual Studio Code
02 NumPy
- Introduction to NumPy
- NumPy Arrays
03 Pandas
- Pandas: Dataframe and Series Basics
- Pandas: Data Cleaning
- Pandas: Data Transformation
- Pandas: Data Aggregation and Grouping
- Pandas: Merging and Joining Data
- Pandas: Handling Categorical Data
04 Matplotlib
- Matplotlib
- Matplotlib: Customization and Styling
05 Scikit-learn
- Scikit-learn: Introduction
- Scikit-learn: Pre-processing Tools
- Scikit-learn: Model Evaluation
- AIML Part1 Quiz 1
06 Seaborn
- Introduction to Seaborn
- Seaborn: Importing and Basic Usage
- Seaborn: Visualizing Univariate Data
- Seaborn: Visualizing Bivariate Data
- Seaborn: Visualizing Categorical Data
- Integration of Seaborn with Machine Learning
07 Types of ML
- Supervised Machine Learning
- Unsupervised machine Learning
08 Supervised Learning: Regression
- Introduction to Regression
- Linear Regression
- Linear regression in One variable
- Linear regression in Multiple Variable
- House Price Prediction using Linear Regression
- Saving Model using Joblib and Pickle
- Polynomial Regression
- Regularization Techniques
- Part 1 : Quiz 1
09 Supervised Learning: Classification
- Classification Algorithm in ML
- Logistic Regression(Binary Classification)
- Logistic Regression(Multiclass Classification)
- Decision Tree Algorithm
- Decision Tree Algorithm: Pruning
- Support Vector Machine
- KNN Classification
- Part 1 : Final Quiz
- Mini-project
Who teaches
Engineers who build, not lecturers who read slides.
KM
Kavita Mali
Robotics Engineer · 6 yrs
Specialises in embedded C, ROS, and capstone mentorship.
KV
Kiran Verma
Robotics Engineer · 4 yrs
Lead instructor for AI & Python tracks.
