Duration - 56 Hours
In-center and online
Machine learning using Python is to provide individuals with the necessary skills to create, train, and deploy machine learning models using Python programming language. This involves learning the fundamentals of machine learning, such as regression, classification, clustering, and deep learning, and understanding how to apply them to real-world problems.
By learning machine learning using Python, individuals can gain the skills needed to analyze large datasets, identify patterns and trends, and make accurate predictions. They will learn how to use popular Python libraries such as NumPy, Pandas, and Scikit-learn, which are widely used in machine learning applications.
The objectives of learning machine learning using Python are:
Understanding the fundamentals of machine learning: This includes learning the different types of machine learning, such as supervised and unsupervised learning, and understanding the key concepts of machine learning, such as overfitting, bias, and variance.
Learning how to use Python libraries for machine learning: This involves learning how to use popular Python libraries such as NumPy, Pandas, and Scikit-learn for data preprocessing, feature engineering, and model training.
Creating and training machine learning models : This involves learning how to create and train different types of machine learning models such as linear regression, logistic regression, decision trees, random forests, and deep neural networks.
Evaluating and optimizing machine learning models : This involves learning how to evaluate the performance of machine learning models and optimize them for better accuracy.
Deploying machine learning models : This involves learning how to deploy machine learning models to production environments and create web-based applications.
Who Can Learn :
Anyone can learn machine learning using Python, regardless of their background or prior experience. However, some basic knowledge of programming and mathematics is recommended.
Data analysts and data scientists : Individuals working in data-related fields can benefit from learning machine learning using Python as it can help them to extract insights from large datasets, identify patterns and trends, and make data-driven decisions.
Software developers : Learning machine learning using Python can help software developers to create intelligent applications that can learn and adapt to user behavior.
Business professionals : Business professionals who want to make data-driven decisions can benefit from learning machine learning using Python as it can help them to identify new business opportunities, reduce costs, and increase revenue.
Students and researchers : Students and researchers who are interested in data science, machine learning, and artificial intelligence can benefit from learning machine learning using Python as it provides them with the necessary skills and tools to conduct research and develop intelligent systems.
Job Opportunities :
Machine Learning Engineer : A machine learning engineer is responsible for designing, developing, and deploying machine learning models using Python. They work closely with data scientists and software developers to build scalable and robust machine learning solutions.
Data Scientist : Data scientists use machine learning models to analyze large datasets and extract insights that can help companies make data-driven decisions. They use Python to preprocess data, build models, and create visualizations to communicate insights to stakeholders.
Data Analyst : Data analysts use Python to extract and analyze data from various sources to provide insights that can help companies make data-driven decisions. They use Python to preprocess data, build dashboards, and create visualizations to communicate insights to stakeholders.
Software Developer : Software developers use machine learning to develop intelligent applications that can learn and adapt to user behavior. They use Python to build models that can classify images, recognize speech, and make recommendations to users.
AI Researcher : AI researchers use Python to develop cutting-edge machine learning algorithms that can solve complex problems. They work closely with data scientists and software developers to develop new models and improve existing ones.
Frequently Asked Questions :
1. What is machine learning and why is it important?
Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed. It is important because it enables businesses to make data-driven decisions, automate tasks, and create intelligent systems.
2. What programming languages are used in machine learning?
Python is the most popular programming language used in machine learning. Other popular languages include R, Java, and C++.
3. What are some popular machine learning libraries in Python?
Some popular machine learning libraries in Python include scikit-learn, TensorFlow, PyTorch, and Keras.
4. Do I need a background in math to learn machine learning with Python?
Yes, some basic knowledge of math is required to learn machine learning with Python. You should have a good understanding of linear algebra, calculus, and probability theory.
5. What are some common applications of machine learning using Python?
Some common applications of machine learning using Python include image recognition, natural language processing, fraud detection, and recommendation systems.
» Preliminaries of statistics
» Python programming for machine learning
» Statistic programming using Python
» Pre-processing using Python
» Supervised learning
• Decision tree
• Support vector machine
• Bayesian network (BNB,GNBtMNB)
• Random Forest
• Logistic regression
• Linear regression
• Random Forest
• Multinomial regression
• Poisson regression
• Negative binomial regression
• Ridge regression
• Lasso Regression
» Unsupervised learning
» Dimension Reduction
» Text mining using NLP
» Survival Analysis
» Deep learning (Neural Network)
• ANN for classification problem
• ANN for regression problem
• RNN for time and sequence related problems
API & Libraries
Scikit-learn, Keras, Tensorflow, Theano, Numpy, Pandas, Matplotlib, Xlrd, Random, Time, StatsModel, NLTK, Stats, Beautiful Soup, Requests, Regex, Pre-processing, Binom, etc.