Data Science and Machine learning Certification Program With AI

This 6-month online program is designed to provide an in-depth understanding of Data Science, Machine Learning, and Artificial Intelligence (AI). Tailored for beginners and intermediate learners, the course emphasizes hands-on learning with real-world datasets, cutting-edge tools, and industry applications. By the end of the program, participants will master the essential skills required to analyze data, build machine learning models, and implement AI-powered solutions for real-world challenges.

Program Overview:

• Duration: 6 months

• Mode: Online

• Goal: Equip learners with essential skills in Data Science and Machine Learning with AI through hands-on projects and real-world applications.

• Target Audience: Beginners to intermediate learners with basic programming and math knowledge.

Why Choose This Program?

It Covers all aspects of Data Science, from data manipulation and visualization to advanced AI techniques like deep learning and neural network.

• Practical Learning

• AI-Driven Applications

• Expert Guidance.

• Career-Ready Training

Program Highlights:

• Interactive Learning:

Week days and Week-end live sessions and recorded lectures for flexibility and interactivity.

• Capstone Project:

Solve a real-world problem using AI and machine learning to develop a deployable end-to-end solution.

• Hands-on Tools:

Work with tools like Python (Pandas, NumPy, Matplotlib, Seaborn).

• Professional Certification:

Receive a recognized certification upon successful completion of the program From SkillSet Arena.

Data Science Introduction/Installation of Software.

Program Structure:

Python

• Introduction.
• Simple Expressions, Variables.
• Data Types/structures /List /Tuples/Dictionary /Sets.
• Branching (If Else Elif).
• Lists.
• Set, Loops, Indexing.
• Tuple.
• Dictionary.
• Break and Continue /string functions/List comprehension.
• Range.
• Functions,args, kwargs.
• Map, Filter.
• Exception Handling.
• Class.
• Modules and Packages, Python Math/Datetime module.

Introduction to Database Management
System(DBMS):

• Introduction to Structured Query Language (SQL).
• Sql Categories.
• Working using SQL commands.
• SQL clause.

Numpy:

• numpyfromfunction.
• data types
• arange and linspace.
• Matrix creation.
• Random number generation.
• Reshaping.
• Indexing and slicing.
• Subseting.
• Universal Functions.
• Broadcasting.
• Array Math.

Pandas:

• Pandas Operations.
• Introduction to Data structure /Series /Dataframe.
• Pandas Basic Functionality /Slicing ,Indexing /Pandas.
• Visualization.
• Read and write from CSV and TSV files.
• Read and write operations from html.
• Creating and working with Data frames.
• Read and write operations from web api.
• Data Cleaning /Missing data handling.
• Data manipulation.
• Date time manipulation.
• Pandas with Database, Table, SQL.
• Merge /Join/Concat operation.
• Data Visualization.

Matplotlib and Seaborn:

• Bar plot.
• Histogram.
• Boxplot.
• Kde plot.
• Scatter plot.
• pi chart.
• Numerical Data Ploting.
• relplot().
• scatterplot().
• lineplot().
• Categorical Data Ploting.
• catplot().
• boxplot().
• stripplot().
• swarmplot().
• Visualizing Distribution of the Data.
• distplot().
• kdeplot().
• jointplot().
• rugplot().
• Linear Regression and Relationship.
• regplot().
• lmplot().

Statistics:

• Types of Statistics.
• Descriptive.
• Inferential.
• Population and Sample.
• Types of Data (Numeric.
• /Continuous/String/categorical/Nominal/Rating).
• Parameter and Statistics.
• (mean, Median,Mode,Std,Variance).
• Uses of variable.
• Types of Variable.
• Continuous.
• Categorical variable.
• Implementation with Numpy and Pandas.
• Percentile.
• Probability.
• Distribution types and Skewness.
• Introduction to Scipy.
• Zscore.
• Implementation with Scipy.
• Hypothesis
• Null Hyppthesis
• Alternate Hypothesis
• Type Error.
• Statistical Tools.
• Ttest (one sample and sample).
• Chi square Test.
• Covariance and Correlation.
• Anova.
• Detecting outliers using boxplot/removing using zscore.

Machine Learning:

• Introduction to Machine Learning.
• Supervised Learning.
• Unsupervised Learning.

Supervised ML:

• Linear Regression.
• Case Study.
• Residuals.
• Simple Linear Regression Model.
• Multiple Regression.
• Standard Scaler.
• Train Test split.
• Model Building and Evaluation metrics (R2 score /mse).
• Logistic Regression.
• Case Study.
• Sigmoid Function.
• Multinomial Logistics Regression.
• Confusion Matrix.
• Accuracy.
• Recall.
• Precision.
• F1 Score.
• Specifity.
• Model Building.

Classification:

• K-Nearest Neighbours concepts.
• Knn model building.
• Naïve Bayes.
• Case study.
• PCA.
• Decision Tree.
• Entropy & Information Gain.
• Gini Indexing (Gini Impurity).
• Model Building.
• Support Vector Machine.
• Case Study.
• Bias /Variance Tradeoff.
• Regularization (Ridge, Lasso).
• Overfitting and underfitting.
• Various Cross Validation techniques.
• Kfold.
• LOOCV.
• Holdout method.
• Encoding Techniques.
• One Hot Encoder.
• Label Encoder.
• Scaling Technique.
• MinMax Scaler.
• Handling missing values.
• SimpleImputer.
• Ensemble methods.
• Bagging.
• Random Forest model.
• Voting Classifier.
• Case Study.
• Boosting Overview.
• AdaBoost implementation.
• Gradient Descent.
• Gradiant Boost overview.
• Gradiant Boost implementation.
• XGBOOST.
• Case Study.
• Finding best params/Hyperparameter tuning techniques.
• GridSearchCV.
• RandomizedSearchCV.
• Pipeline.

Unsupervised ML:

• K-Means Clustering overview.
• Kmeans model.
• Hierarchical clustering.
• DBSCAN.
• Job Simulation Program.

Intro to Deep Learning –Neural Network(AI):

This program is designed to be beginner-friendly but also provide enough depth to give students a solid understanding of Data Science and Machine learning(AI) With hands-on projects and regular assessments, learners will have the opportunity to apply their skills in real-world scenarios.