BASIC TECHNICAL PROWESS

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• PYTHON - Applications of machine learning, deep learning

• POSTGRE SQL
• MYSQL

• MS EXCEL
• GOOGLE SHEETS

• GIT
• MS POWERPOINT
• MS WORD
• MS FORMS
• MS OUTLOOK
• MS SWAY
• MS VISIO
• GOOGLE DOCS
• GOOGLE SHEETS
• GOOGLE SLIDES

DATA HANDLING (Using Excel and Python: Pandas, Numpy, Matplotlib, Seaborn)

1. Data Treatment, Data Cleaning, Data Sanity Checks, Feature Engineering, Data scaling, Data Transformation, Outlier Treatment, Anomaly detection and treatment, Pattern Identification, Data Preperation
2. Creating engaging data stories to reach the root cause of business problem using advanced visualizations

MACHINE LEARNING (Using Excel and Python: sklearn, statsmodels)

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Bagging and Boosting
5. Segmentations (K Means and K Mode)
6. Time Series Analysis (Holtwinter's Method and SARIMA)

DEEP LEARNING AND COMPUTER VISION (Using Python: Tensorflow, Keras)

1. Dense Neural Networks
2. Convolutional Neural Networks
3. Transpose Convolutions
4. Recurrent Neural Networks (LSTM and GRU)
5. Semantic Segmentation
6. Transfer Learning

GENERATIVE ADVERSARIAL NETWORKS

1. Cycle GAN
2. DC GAN
3. MuseGAN
4. SAGAN

ENCODERS:

1. Autoencoders
2. Variational AutoEncoders

TRANSFORMERS:

1. BERT,
2. GPT2