Artificial Intelligence with Python
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What Will You Learn?
About This Course
To succeed in AI, freshers should have a strong foundation in computer science, mathematics, and statistics along with the capability to work with big data, as AI requires processing and analyzing large amounts of data to identify patterns and make predictions. To equip students with the necessary expertise and abilities to excel in the rapidly expanding AI domain, we are thrilled to offer an all-encompassing Introduction to Artificial Intelligence Course. The course curriculum covers a broad spectrum of subjects, such as Machine Learning, Natural Language Processing (NLP), and robotics. Students will learn about cutting-edge AI technologies and techniques and how to apply them to practical challenges. During the 2 months program, students will obtain valuable practical exposure to AI tools and technologies and will have the opportunity to develop their own AI projects. However, the advantages of our AI course are not confined to just technical knowledge. By enrolling in this course, students will also gain vital analytical and problem-solving abilities that are indispensable in today's fast-paced, data-oriented world. Students will gain insights into how AI can be leveraged to enhance decision-making, optimize efficiency, and foster innovation in any field.
Requirements
FAQ
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Curriculum Overview
This course includes 2 modules, 21 lessons, and 20:00 hours of materials.
Module : Python Fundamentals
Topic : Setup & Basics
Detailed Concepts Covered : Install Python/IDE, Scripts vs Notebooks, Syntax, Variables, Input/Output, Data Types, Operators
Topic : Core Types
Detailed Concepts Covered : Numbers, Strings, Lists, Tuples, Sets, Dictionaries, Mutability, Indexing, Slicing
Topic : Conditionals & Loops
Detailed Concepts Covered : If/elif/else, Logical Operators, Nested Conditions, Common Patterns, For/While Loops, Break/Continue/Pass
Topic : Modularity & Exceptions
Detailed Concepts Covered : User-defined Functions, Arguments & Return, Recursion, Try/Except/Finally, File Handling Basics
Topic : Iteration Patterns
Detailed Concepts Covered : List Comprehensions, Dict Comprehensions, Set Comprehensions, Loop Refactoring, Common Python Patterns
Topic : Arrays & Vectorization
Detailed Concepts Covered : Ndarray Creation, Indexing & Slicing, Reshape, Broadcasting, Vectorized Operations
Topic : DataFrames & Cleaning
Detailed Concepts Covered : Series & DataFrame creation, Indexing & Selection, Filtering, Handling Missing Data, Derived Columns
Topic : Joins & Aggregations
Detailed Concepts Covered : Merge, Concat, Groupby, Aggregations, Pivot/Tidy Data Patterns, Basic Statistics
Topic : Matplotlib Basics
Detailed Concepts Covered : Line, Bar, Scatter, Histogram, Box Plots, Styling, Labels, Legends, Annotations
Topic : Seaborn & Review
Detailed Concepts Covered : Seaborn Plots (Distplot, Pairplot, Heatmap), Customizations, Layouts, Review Python & Data Concepts
Topic : DL Environment
Detailed Concepts Covered : AI Use-Cases, Conda/Venv, Install Numpy/Torch/Torchvision/OpenCV, Import Checks
Topic : Neurons & Paradigms
Detailed Concepts Covered : Artificial Neuron, Supervised/Unsupervised/Rl, Scope Of Deep Learning
Topic : Architecture & Forward Pass
Detailed Concepts Covered : ML Vs DL, Layers, Weights/Bias, Activation, Manual Forward Pass
Topic : Nonlinear Functions
Detailed Concepts Covered : Ramp/Sigmoid/Gaussian/Relu, Vanishing/Saturation, Gradient Behavior
Doubt classes and managing pending topics
Topic : Optimization
Detailed Concepts Covered : Loss, Gradient Descent, Stochastic Updates, Backprop Mechanics
Topic : PyTorch MLP
Detailed Concepts Covered : Model Definition, Training Loop, Loss/Accuracy Tracking, Confusion Matrix
Topic : Image Preprocessing
Detailed Concepts Covered : Image I/O, Resize, Blur, Threshold, Augmentation (Rotate/Flip)
Topic : Convolution & Pooling : Detailed Concepts Covered : Kernels, Stride/Padding, Pooling, Flattening, Receptive Fields
Topic : Compact CNN AND End-to-End Demo
Detailed Concepts Covered : Two Conv Blocks, Training On Mnist/Cifar-10 Subset, Evaluate/Inspect Filters, Small Vision Or Tabular Task, Training, Evaluation, Brief Presentation
Hi everyone
With this attachment, we have 10 projects available.
Please complete any 2 projects from the list and submit them as per the instructions shared.
If you have any questions or need clarification, feel free to reach out.
Thank you.
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