Data Science and Machine Learning Training using Python
Data Science and Machine Learning Course using Python encompasses a diverse array of applications, such as forecasting customer behavior, optimizing business operations, and enhancing decision-making across multiple sectors. It is employed in activities including fraud detection, risk evaluation, and tailored recommendations. Furthermore, data science is vital in domains such as healthcare, finance, marketing, and technology.
Overview
Data Science and Machine Learning Course using Python – In our Online Training Course you can learn topics like Data Aquisition, Exploratory Data analysis ,Supervised Algorithm, Model Evaluation , Unsupervised Learning Algorithm, Time Series Analysis and Forecasting, Natural Language Processing , Neural Networks and Deep Learning, Python, Numpy, Matplotlib, Seaborn, Plotply, Pandas, Linear Regression, Lasso Regression, Ridge Regression, Logistic Regression , Elastic Net Regression, Cross Validation, Hyper parameter Tuning, Decision Tree ,SVC, SVM, Naive Bayes, Random Forest ,RCNN, Yolo Siamese, DCGAN, WGAN Tokenization, Stemming, Lematization, RNN , LSTM ,Transformers , BERT ,Adaboost, Xgboost, Gradientboost , KNN, PCA ,Deep Learning(Neural Networks) Optimizers, Tensorflow, Pytorch
CNN, Alex Net, LeNet, ResNet,
Diploma in DATA SCIENCE AND MACHINE LEARNING
Data science and machine learning involve the exploration, analysis, and extraction of insights from large datasets, leveraging statistical techniques and algorithms to develop predictive models and drive informed decision-making across various domains
You will be learning Python Programing with libraries such as NumPy, pandas, scikit-learn, TensorFlow, and Keras, R with packages like caret and ggplot2, SQL for data querying, Tableau, Power BI, Matplotlib, Seaborn, and Plotly for data visualization.
Data Visualisation
Data visualization is the graphical representation of data to communicate insights, patterns, and trends effectively, enabling stakeholders to understand complex information quickly and make informed decisions.Introduction to popular data visualization tools and software, including Tableau, Power BI, Matplotlib, ggplot2, Plotly, and D3.js.Visualization techniques for representing geographic data, including choropleth maps, point maps, and interactive map visualizations.Creation of dashboards and interactive storytelling techniques to communicate insights effectively to a target audience.Preprocessing and cleaning data for visualization, including techniques for handling missing values, outliers, and data transformations.
SQL (Structured Query Language) plays a crucial role in data visualization by enabling users to extract, manipulate, and aggregate data from relational databases, which can then be visualized using various visualization tools and libraries.
Who Should Learn Data Science and Machine Learning
Learning data science and machine learning offers several benefits for students:
- High Demand: Data science and machine learning skills are in high demand across industries, offering abundant career opportunities and competitive salaries.
- Versatility: These skills are versatile and applicable across various domains, including finance, healthcare, e-commerce, and more, allowing students to pursue diverse career paths.
- Problem-Solving Skills: Students develop strong analytical and problem-solving skills through hands-on experience with real-world data analysis and predictive modeling tasks.
- Innovation: Data science and machine learning drive innovation by enabling organizations to extract valuable insights from data, identify trends, and make data-driven decisions.
- Personalization: Students can leverage data science and machine learning techniques to create personalized experiences in areas such as recommendation systems, personalized marketing, and personalized learning platforms.
- Future-Proofing: With the increasing importance of data-driven decision-making, students equipped with data science and machine learning skills are well-positioned to adapt to evolving industry trends and technological advancements.
- Contribution to Society: Data science and machine learning have the potential to address societal challenges in areas such as healthcare, environmental sustainability, and social welfare, allowing students to make a positive impact on the world.
