Our AI and Applied Data Science with Python training course is designed to provide students with the skills and knowledge necessary to build and implement data-driven solutions using Python. This course is ideal for individuals with an interest in data science and AI and those seeking to enter the field.
What is the Course About?
This course is designed to provide you with a comprehensive understanding of Artificial Intelligence (AI) and Applied Data Science using Python. The course covers a wide range of topics, including the fundamentals of data analysis, machine learning, and deep learning. You will learn how to use Python to manipulate and analyze data, build predictive models, and deploy machine learning algorithms.
What Will You Learn?
You will learn how to use Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn to perform data analysis and build predictive models. You will also learn how to apply machine learning algorithms such as decision trees, logistic regression, and neural networks to solve real-world problems. In addition, you will learn how to evaluate the performance of your models and optimize them for better accuracy.
Skills You Will Gain
After completing this course, you will have gained the following skills:
- Proficiency in Python programming
- Data analysis and manipulation using Python libraries
- Understanding of machine learning algorithms and their applications
- Ability to build predictive models and deploy machine learning algorithms
- Evaluation and optimization of machine learning models
Overall, this course will provide you with a strong foundation in AI and Applied Data Science with Python, giving you the necessary skills to tackle real-world problems using data-driven solutions.
Why should I enroll into this course?
Undertaking a training course in AI and Applied Data Science with Python can provide numerous benefits. Here are some of them:
- High demand for AI and Data Science skills: AI and Data Science are some of the fastest-growing fields in today’s job market. Companies across industries are looking for professionals with expertise in these areas. By taking a training course in AI and Applied Data Science with Python, you can enhance your job prospects and increase your chances of getting hired.
- Hands-on experience with Python: Python is a popular programming language used extensively in AI and Data Science. By taking a training course in AI and Applied Data Science with Python, you can gain hands-on experience with the language and its libraries, such as NumPy, Pandas, Matplotlib, and Scikit-learn. You can also learn to work with big data tools such as Hadoop, Spark, and TensorFlow.
- Learn essential Data Science concepts: The training course in AI and Applied Data Science with Python provides a comprehensive overview of essential concepts in Data Science, such as data cleaning, data analysis, and data visualization. You can learn how to use various Machine Learning algorithms, such as K-Nearest Neighbors, Linear Regression, Logistic Regression, and Decision Trees. You can also learn how to work with deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- Enhance problem-solving skills: AI and Data Science require strong problem-solving skills. By taking a training course in AI and Applied Data Science with Python, you can learn to identify problems, formulate hypotheses, design experiments, and analyze data. You can also learn to evaluate model performance, optimize model parameters, and interpret model results.
- Flexibility: Most training courses in AI and Applied Data Science with Python are available online, which means you can learn at your own pace and schedule. You can also choose the topics that interest you the most and skip the ones you already know. With an online course, you can learn from anywhere, anytime, and on any device.
Career Pathways, Average Salary and Hiring Companies:
After undertaking a course in AI and Applied Data Science with Python, there are several career pathways you can pursue, including:
- Data Scientist: A Data Scientist is responsible for collecting, analyzing and interpreting large and complex data sets to extract insights and make informed decisions. The average salary for a Data Scientist in India ranges from 3.8 Lakhs per annum to nearly 26 Lakhs per annum, with the average annual salary being reported as 10 Lakhs, as per the latest Ambition Box statistics. Major hiring companies include Accenture, Deloitte, Fractal Analytics, Genpact, IBM, Infosys, KPMG, TCS, and Wipro.
- Machine Learning Engineer: A Machine Learning Engineer designs, develops, and implements machine learning algorithms and models that can learn from data and make predictions. The average salary for a Machine Learning Engineer in India is around ₹9,00,000 per annum. Major hiring companies include Amazon, Google, IBM, Microsoft, Nvidia, and Qualcomm.
- AI Engineer: An AI Engineer builds and deploys artificial intelligence systems and applications that can simulate human intelligence and behavior. The average salary for an AI Engineer in India is around ₹9,00,000 per annum. Major hiring companies include Accenture, Deloitte, Genpact, IBM, Infosys, TCS, and Wipro.
- Big Data Analyst: A Big Data Analyst analyzes and interprets large and complex data sets to identify patterns, trends, and insights that can be used to make informed decisions. The average salary for a Big Data Analyst in India is around ₹6,00,000 per annum. Major hiring companies include Accenture, Capgemini, Cognizant, Deloitte, IBM, Infosys, TCS, and Wipro.
- Business Intelligence Analyst: A Business Intelligence Analyst is responsible for analyzing business data to identify trends and patterns, and create reports and visualizations that can be used to make informed decisions. The average salary for a Business Intelligence Analyst in India is around ₹5,00,000 per annum. Major hiring companies include Accenture, Capgemini, Cognizant, Deloitte, IBM, Infosys, TCS, and Wipro.
Note that the above-mentioned salaries are only indicative and may vary depending on various factors such as location, experience, skillset, and company size.
- The course begins with an overview of the history and background of Python programming, followed by an introduction to the basic syntax, variable types, and control flow. Students will also learn about lists, tuples, dictionaries, basic operators, and file I/O and exceptions.
- Next, the course delves into Python programming for data science, covering NumPy and Pandas for data manipulation, Matplotlib for data visualization, and Scikit-learn for building machine learning models. Students will learn about model evaluation and selection, cross-validation, confusion matrices, basic evaluation metrics, precision-recall, ROC curves, overfitting and underfitting, and data leakage.
- The course also covers supervised and unsupervised learning techniques such as K-Nearest Neighbors, Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, Neural Networks, Naive Bayes Classifiers, Random Forests, and Gradient Boosted Decision Trees. Students will also learn about dimensionality reduction, clustering, and text mining using regular expressions and natural language processing.
- Optional topics in this course include computer vision, deep learning, popular tools like Redis, Cassandra, and MongoDB, cognitive computing using cloud platforms such as IBM Watson, Microsoft Azure, Amazon Al, and Google Cloud ML, and other miscellaneous topics like Jupyter, Matplotlib, OpenCV, Keras, and NLTK.