Duration: 3 months
Machine Learning / Artificial Intelligence
About Profound Machine Learning / Artificial Intelligence Classes?
According to the survey data scientist is the top ranking professional in the market. As a Data Scientist you need to understand the Business problem, gather data for the same and analyze by applying correct algorithms and techniques using current tools. A after running data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report.
Need of Data Science
- Data Science is basically used to take better decision.
For example, whether to buy Self Driven Car or not. Current Data analysis shows it will root out more than 2 million deaths caused due to car accidents
- Predictive Analysis
What Will Happen Next
- Pattern Discovery
Is there any hidden information in the data. For example, to analyse patterns of sales that is, in which month it is increasing
What is Data Science?
- Asking the right Questions and Exploring the data
Basically, you need to know what problem you are solving that is asking the right question. After asking questions you will have data for that as input and you will perform certain action on it
- Modeling the data using various algorithms
Suppose if you need to perform machine learning you have to decide which Model to use and which algorithms to use and then you need to train the model and so on.
- Finally Communicating and Visualizing the results.
After running the data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report. All the information that has been gathered
Why learn Data Science course at Profound Edutech?
- Data Science Course at profound is designed to train students and professional in the industry's most widely sought after skills, and make them job ready in the field of Data Science.
- We at profound Edutech provide you with an excellent platform to learn and explore the subjects from Industry experts.
- 100 % placement assistance for the trained candidates.
- Training by experienced faculties
Eligibility: IT Professionals / Exposure to Information Technology
- Introduction to Python Programming
- Installation & working
- Basic Operators, Data types, Variables
- Control Statements & Conditional Looping
- Functions
- Collections in Python
- Object Oriented Programming
- Modules and Packages
- String
- File Handling
- Exception Handling
- NumPy
- Introduction, installation
- 1D & 2D arrays
- Array indexing – slicing & advance
- Operations – Arithmetic, Logical, Math, String, Statistical, Set, Broadcasting
- Pandas
- Introduction, installation
- Series – Creation, indexing, slicing, attributes & functions
- Dataframes – Creation, operations, merging dataframes, Concatenate dataframes, binary operations
- Data input and output
- Matplotlib
- Introduction, installation
- Data Visualization
- Plots – single line, multiple line
- Grid axes, Labels, color line markers
- Seaborn
- Distribution plots
- Category plots
- Matrix plots
- Grids, Regression Plots
- Introduction to plotly, Altair, ggPlot
- What is Machine learning?
- Machine Learning Methods - Predictive Models, Descriptive Models
- Regression
- Simple Linear Regression
- Multiple Linear Regression
- Bias-Variance trade-off
- Classification
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- SVM
- Decision Trees
- Random Forest
- Clustering
- K-means
- Hierarchical
- DBSCAN
- Dimensional Reduction
- Linear discriminant analysis
- Principal component analysis
- Neural Networks
- Introduction to Neural Networks
- Back propagation
- Maths of neural networks
- Conventional Neural Networks (CNN)
- Introduction to Image processing
- Basic convolution
- Convolution Neural Network Application
- Fine tuning
- Recurrent Neural Networks (RNN)
- Introduction to Recurrent Neural Networks
- Application in Time series and text Analytics
- Convolution Neural Network Application
- Fine tuning
- Natural Language Processing with Deep Learning
- Introduction to NLP
- Text representation with DL
- Text classification, Grammar detection, Sentiment analysis with Deep Learning
- What is Gen AI? History and trends
- Difference between Discriminative vs Generative Models
- LLMs overview: GPT, Gemini, Claude, LLaMA, Falcon
- Pre-training, Fine-tuning, RLHF
- ChatGPT (OpenAI)
- Google Gemini
- Hugging Face Transformers (beginner)
- LangChain (Intro)
- Prompt Engineering Basics
- Text summarizer using ChatGPT API
- Conversational bot for CSV file (Q&A using ChatGPT)
- Generating synthetic data for ML training
- Gen AI for report creation & code debugging
- Clean data (Python)
- Build ML model (scikit-learn)
- Create interactive dashboard (Power BI)
- Auto-generate report using ChatGPT prompts
Capstone Project
- ML + Power BI + Gen AI Project
- Clean data (Python)
- Build ML model (scikit-learn)
- Create interactive dashboard (Power BI)
- Auto-generate report using ChatGPT prompts
Batch Schedule
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