About Python & Machine Learning Course
The best thing about python is that you can use it for everything from web development, to data science and data visualization, to games development, and DevOps, without having to start at ground zero and implement your own code for everything.
This course will help you understand both basic & advanced level concepts of Python like writing python scripts, sequence & file operations in python, Machine Learning, Data Analytics, Web application development & widely used packages like NumPy, Matplot, Scikit, Pandas & many more. Professionals who don’t have good coding skill need not worry, Python is the most user-friendly and easy to learn language, is used as a powerful tool in handling advanced analytics applications.
- Gyansetu trainers are well known in Industry, they are highly qualified working professionals in MNCs, having a wide experience in training industry.
- We provide interaction with faculty before the course starts.
- Our Train the Trainer approach ensures you learn proactively and come out as an expert.
- We are open seven days a week and provide 24×7 Lab Support Services.
Pre-requisites for Python & Machine Learning Course
After the completion of Course, you will be able to:
- Develop and Implement various Machine Learning Algorithms in daily practices & Live Environment.
- Build Real time Machine Learning Applications
- Implement Data Analytics models on various Data Sets
- Data Mining across various file formats using Machine Learning models
- Building Recommendation systems and Classifiers
- Perform various type of Analysis (Prediction & Regression)
- Implement plotting & graphs using various Machine Learning Libraries
- Import data from HDFS & Implement various Machine Learning Models
- Building different Neural networks using NeuPy and TensorFlow
Who should learn Python & Machine Learning?
1. Testing professionals
2. Senior IT Professionals
3. BI /ETL/DW professionals
4. Developers and Architects
5. Mainframe professionals
Project #1: Sentiment Analysis for the Election Prediction
Industry : Politics
Various Steps for Sentimental Analysis:
- Creating the text data from various UCI repository .
- Create the corpus and convert into structural data.
- Tokenize the structural data using various NLP packages.
- Analyze the sentiment of token by polarity checking.
- Compare the negative and positive polarity of text and graph generation on polarity checks.
Project #2: IPL Match Prediction
Industry : Entertainment
- Import the text data from the various sites.
- Collect the structural data for the each IPL team.
- Analysis the text data and structural data.
- Use Statistical Inference (Linear Regression Model) system on the formatted data.
- Make a confusion matrix for the each team.
- Preparation of probability graph of each team.
Project #3: Cluster Generation for the Text Data
- Collect the data from the shopping sites.
- Get the age of different user.
- Make a cluster of different age groups.
- Associate the shopping preference with the age group (K-means clustering)
- Generate the cluster of different age groups with their shopping frequency of items.
- Generate the sales of items according to the season clusters.