Data Science Course with Machine Learning, Deep Learning & AI

Data from commercial research has always had a say on how manual tasks are conducted in the pharmaceutical industry. With advancements in machine learning and artificial intelligence, data now plays an even more significant role in prescribing the best medicines for very specific medical conditions. Prescribing the right medicine is essentially a classification problem. At Acadgild, we focus on Image Analytics, NLP, Text Analysis and Predictive Analytics using CART (Classification and Regression Tree) models.

Data is arguably most influential in the e-commerce industry. Needless to say that machine learning and artificial intelligence play a crucial role in the development of this domain. These technologies help gather, analyse, and make sense of data regarding customer habits in real-time to make suitable predictions. For instance, the moment someone adds a product to their cart, websites and e-commerce platforms can recommend other products that the same customer is most likely to buy thereafter. Machine learning and artificial intelligence is also useful in anomaly detection, customer care and pricing. Acadgild issues several case studies that involve churn predictions, sentiment analysis, customer segregation and time series forecasting – all of which are crucial in e-commerce.

Data science relies on information from a variety of sources to make effective predictions. In a well-documented domain like finance, it has created plenty of opportunities to understand customers and deliver better products and experiences. Tasks like generating a credit-score for potential customers has become a lot easier. Acadgild uses case studies from finance to help learners understand the practical implementations of feature engineering techniques and other machine learning concepts to solve real-world problems.

Machine learning is transforming the telecommunications industry. It is having a significant impact in network automation, and more importantly, on customer experience. The technology is also reducing operational costs and helping telecom companies achieve greater margins with increased efficiency. Acadgild provides case studies on telecommunications companies that provide internet, phone, TV streaming and movie streaming services. These case studies help learners find models that can effectively predict customer behaviour using clustering techniques and deep learning.

Managing a supply chain has never been easy. Thanks to machine learning, however, we can now use algorithms to discover patterns in demand and other factors to ensure that there is never a shortage of goods and products. Algorithms deal with query data in an iterative fashion and improve the accuracy of predictions greatly. Acadgild covers various case studies on demand forecasting, planning management, etc.

Apply Regression techniques to a European Soccer Database and give soccer players an overall rating based on attributes like ‘crossing’ and ‘finishing’. Learn to use Python, Numpy, Pandas, Scipy, Scikit, and Regression models.

Use popular Data Cleaning libraries in Python like Pandas, Numpy, Matplotlib, etc., to Clean and Visualize meteorological data. Practice Data Handling, Manipulation and Visualization in this project.

Apply Regression techniques to a European Soccer Database and give soccer players an overall rating based on attributes like ‘crossing’ and ‘finishing’. Learn to use Python, Numpy, Pandas, Scipy, Scikit, and Regression models.

Run Census Bureau Data through Classification algorithms to estimate the salaries of individuals in the database. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit and Classification algorithms.

Use Clustering algorithms to determine how different stocks will perform and how they correlate with each other based on Stock Market Data. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, Clustering models.

Apply the Arima forecasting model to predict how stocks of various companies will perform. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, and Time Series algorithms.

Preprocess images and train a Convolutional Neural Network on all samples. The dataset will contain images of airplanes, dogs, cats and other objects. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, CNN.

Create a model that uses description of TV show episodes to generate a script for a new episode. Script your own episode of the Simpsons using RNNs and descriptions of episodes from all 27 seasons. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, RNN.