What You will learn in this program ?
5 Core Fundamental Self Paced Courses + LIVE Bonus as below
- Introduction to AI | How to create basic AI application
- How to install Python & Libraries & Basics of python Programming
- Introduction to Computer Vision
- Moving Object Detection and tracking
- Face Detection and Tracking
- Object Tracking based on color
- Face Recognition
- Face Emotion recognition
- Introduction to Deep learning
- Designing your First Neural Network
- Object recognition from Pre-trained model
- Image classification using CNN
- Hand gesture recognition
- Leaf disease detection
- Character recognition using CNN
- Label reading using Optical Character Recognition OCR
- Smart Attendance system
- Vehicle detection
- License plate recognition
- Drowsiness detection
- Road sign recognition
- Introduction to Machine learning
- Evaluating and Deploying the various ML model
- Fake news detection
- AI snake game design
- Introduction to NLP & it’s Terminology
- Title Formation from the paragraph
- Speech emotion analysis
- Cloud based A.I
- A.I using Hardware
- Introduction to A.I & Machine Learning
- Exploring Various Python Notebooks
- Sale Prediction using LOGISTIC REGRESSION
- Salary estimation using K-NEAREST NEIGHBOUR
- Handwritten Digit Recognition using SUPPORT VECTOR MACHINE CLASSIFIER
- Titanic Survival prediction using NAIVE BAYES
- Plant leaf Iris detection using DECISION TREE
- Digit recognition using RANDOM FOREST
- Evaluating Classification model Performance Project
- Breast Cancer Detection using various ML Algorithm – Evaluation
- House price prediction using Linear Regression Single Variable
- Exam mark prediction using LINEAR REGRESSION – MULTIPLE VALUES
- Salary Prediction using POLYNOMIAL REGRESSION
- Stock Price Prediction using SUPPORT VECTOR REGRESSION
- Height Prediction using DECISION TREE REGRESSION
- Car price prediction using RANDOM FOREST
- Evaluating Regression model performance
- Regression Model Selection for Engine Energy prediction.
- Identifying the Pattern of the Customer spent using K-MEANS CLUSTERING
- Customer Spending analysis using HIERARCHICAL CLUSTERING
- Clustering Plant Iris Using Principal Component Analysis
- Movie Recommendation System Using Singular Value Decomposition
- Market Basket Analysis using APIRIORI
- Market Basket Optimization/Analysis using ECLAT
- Web Ad Optimization using Upper Confidence Bound – Reinforcement Learning
- Sentimental Analysis using Natural Language Processing
- Breast cancer Tumor prediction using XGBOOST
- Introduction to Deep Learning & Diabetes detection using Simple Neural Network
- Covid-19 Detection using CNN
- A.I Snake Game using REINFORCEMENT LEARNING
- Road Map to become a Data Scientist
- Data Preparation – Power Query & Tables
- Data analytics- Formula & Pivot Table
- Story Telling – Charts & Dashboard
- Automation – VBA Macros & Power Query
- Descriptive Statistics
- Probability – Permutations, Combinations
- Population and Sampling
- Probability Distributions
- Hypothesis Testing & ANOVA
- Connect Tableau to a Variety of Datasets
- Data Visualization
- Connect Power BI to a Variety of Datasets
- Data Visualization in Powerbi
- Introduction to Python
- Basic Python Programming
- Python Numpy functions
- Pandas for Data analytics in Python
- Matplotlib for data visualization
- Seaborn for data visualization
- Kaggle Dataset and Notebooks
- SQL basics for Data analytics – Part-1
- SQL basics for Data analytics – Part-2
- MongoDB basics for Data analytics
- Introduction to Machine Learning
- Evaluating and Deploying M.L Algorithms
- Introduction to Deep Learning
- Covid-19 Detection using X-Ray Images with CNN
- Tag Identification system using NLTK
- Introduction to Python
- Installing & Working with Python IDLE
- Configuring Environmental Variables – Command Window
- Installing Anaconda Navigator (Jupyter Notebook), Anaconda Navigator (Spyder Notebook), Google Colab, Pycharm
- Working with Libraries
- Simple Arithmetic
- Introduction to Strings Indexing and Slicing with Strings
- String Properties and Methods Print Formatting with Strings
- Lists, Dictionaries, Tuples, Sets, Booleans
- Python Objects and Data Structures
- If Elif and Else , For Loops, While Loops, Functions
- Tuple Unpacking
- *args and **kwargs
- Lambda Expressions, Map, and Filter Functions
- Attributes & Class Keyword
- Class Object Attributes and Methods
- name and “main”
- Errors and Exceptions Handling
- Decorators
- Generators
- Collections Module
- Opening and Reading Files and Folders
- Datetime Module, Math and Random Modules
- Debugger, Regular Expressions
- Zipping and Unzipping files with Python
- Setting Up Web Scraping Libraries
- Grabbing an Image
- Introduction to Images with Python
- Working with CSV Files in Python
- Introduction to Deep Learning
- Basic Computer Vision
- Neurons & Perceptron
- Activation Function
- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
- Artificial Neural Network – Project 1
- Optimization Algorithms – SGD, Momentum, NAG, Adagrad, Adadelta, RMSprop, Adam
- Batch Normalization
- Hyperparameter tuning
- Interpretability
- Deep Neural Network – Project 2
- Convolutional Neural Network & its Layers
- CNN Architecture
- Different frameworks on Deep Learning (Tensorflow, Keras, PyTorch & Caffe)
- Object Recognition using Pre Trained Model – Caffe – Project 3
- Image classification using Convolutional Neural Network from Scratch – Tensorflow & Keras – Project 4
- Custom Image Classification using Transfer Learning – Project 5
- YOLO Object recognition – Project 6
- Image Segmentation – Project 7
- Project using MxNet – Project 8
- Project using PyTorch – Project 9
- Social Distancing detector – Project 10
- Face Mask detector – Project 11
- Introduction to RNN and LSTM
- Project using RNN – Project 12
- Introduction CUDA Toolkit and cuDNN for deep learning
- Getting started with the Intel Movidius Neural Compute Stick – Project 13
- Custom Object classification using Nvidia Jetson – Project 15
