Applied Data Science with AI/ML
Duration: 120 Hrs
Level 1 – 60 Hrs
Level 2 – 60 Hrs
Program Objective :
To equip learners with industry-relevant technical skills and enhance their job readiness through project-based learning, hands-on tool exposure, and real-world application deployment, thereby preparing them for successful employment in core domain areas
Program Outcomes:
- Perform exploratory data analysis (EDA) using Pandas including data cleaning with proficiency in handling large datasets.
- Build and evaluate machine learning models, including regression, classification, and clustering algorithms, and apply them to solve practical business problems.
- Develop projects that demonstrate the ability to apply learned concepts
Modules:
Level 1 – Data Analysis 60 Hrs:
- Exploratory Data Analysis using Pandas
- Machine Learning
- Python (Pre-recorded video)
Level 2 – Deep Learning and NLP 60 Hrs:
- Deep Learning
- Natural Language Processing (NLP)
Project stream:
- Machine Learning and Deep Learning Deploy using Streamlit or Huggingface
Experiential Project Based Learning:
- End-to-End AI Project Experience
Platform:
- Google Colab / Jupyter Notebook
| LEVEL 1: Data Analysis (60 Hours) | ||
|---|---|---|
| Module 1: Exploratory Data Analysis with Pandas (30 Hours) | ||
| NumPy | Vectorization | Broadcasting |
| Slicing of Matrices | Filtering | Array Creation Functions |
| NumPy Functions across axis | Stacking of arrays | Matrix Calculation |
| Pandas Series | Data Cleaning | Handling Missing Data |
| Pandas Data frame | Selection Data (loc, iloc) | Filtering Data Frames |
| Sorting Data Frames | Importing Data | Data Visualization |
| Working with Categorical Data | Grouping & Aggregation | Merging Data Frame (concat, merge) |
| Module 2: Machine Learning (30 Hours) | ||
| Understand what is Machine Learning | Regression | Logistic regression |
| Supervised machine learning | Simple linear regression | Naïve Bayed Classification |
| Unsupervised machine learning | Multiple linear regression | Decision tress and its types |
| Train test split the data | Performance measure for regression | K Nearest Neighbor Classification |
| ML Workflow for project implementation | MSE, R-Squared, MAE, SSE | Performance Measure for Classification |
| Classification | Various types of classification | Accuracy, Recall, Precision, F1measure |
| Sorting Data Frames | Importing csv files | Importing Excel Files |
| LEVEL 2: Deep Learning and NLP (60 Hours) | ||
| Module 3: Deep Learning (30 Hours) | ||
| What is Deep Learning | Hidden Layers | Building project based on CNN |
| Deep Learning Methods | Activation Function | Tensorflow, pytorch, Keras |
| Deep Learning Application | Forward and Backward propagation | Batch Normalization, dropout |
| Artificial Neural Network | Deep Learning Libraries | Performance measure for ANN |
| CNN architecture | CNN for computer vision | Object detection |
| Computer vision basics | OpenCV | Working with Images |
| Module 4: Natural Language Processing (30 Hours) | ||
| Natural Language processing | NLP Applications | Regular Expression |
| Tokenization, stopwords | Stemming, Lemmatization | Word Embeddings |
| NLTK | POS Tagging NER | bag of words, TF-IDF, unigrams, bigrams |
| RNN, RNN architecture | Bidirectional LSTM – Encoders and Decoders | Text classification using ML |
| Project: Machine Learning and Deep Learning | ||
| Apply statistical methods to make decision in various business problems, including bank, stock market | Apply regression to predict future flight price | Apply classification to classify customer churn |
