Applied Machine Learning Using Python – Data Science & AI Career Certification

The Applied Machine Learning Using Python Certification Program is a comprehensive 12-week, 72-hour live online course designed to help learners build a strong foundation in Python programming, Data Science, Machine Learning, Deep Learning, and AI applications. This program is ideal for individuals looking to enter the fast-growing fields of Data Science, Machine Learning, and Artificial Intelligence with 100% job-readiness.

Python is the core gateway to today’s most in-demand technologies, and this course prepares learners to master Python from the fundamentals to advanced ML concepts. You will work on real case studies, hands-on machine learning projects, and end-to-end model-building workflows, enabling you to confidently process data, build predictive models, visualize insights, and solve real-world business problems.

The curriculum covers essential Python programming concepts, core statistics for analytics, data visualization techniques, supervised and unsupervised machine learning algorithms, neural networks, and deep learning fundamentals—including ANN, CNN, RNN, and LSTM networks.

By the end of the program, learners will be able to analyze datasets, build predictive machine learning models, implement neural networks using TensorFlow/Keras, and present insights effectively—skills highly valued across industries such as technology, finance, healthcare, e-commerce, and consulting.

This is a job-oriented, project-based training program designed to help you start a successful career in Data Science and Machine Learning.

What You Will Learn (Based Entirely on Your Provided Syllabus)

1. Data Science Fundamentals

• What is Data Science?

• Data Science vs Big Data Analytics

• Types of Data

• Data Science Lifecycle

• Data acquisition, modeling, visualization, and business roles

2. Essential Statistics for Data Science

• Analytics concepts

• Central tendency, dispersion, skewness & kurtosis

• Correlation & covariance

• Probability theory

• Distributions

• Hypothesis testing, sampling & estimation

3. Python Programming

• Installation, IDEs, input/output

• Data types, loops, operators

• Lists, tuples, sets, dictionaries

• Functions, lambda, recursion

• OOP concepts: classes, objects, constructors, inheritance

• File handling & Pickle module

4. Data Analysis & Visualization

• NumPy arrays, operations & indexing

• Pandas series & dataframes

• GroupBy, missing values

• Matplotlib visualizations:

  • Scatter, boxplots, histograms, bar charts, styling

5. Machine Learning Algorithms

• Supervised, Unsupervised & Reinforcement learning

• Linear & Logistic Regression

• KNN, SVM, Naïve Bayes, Decision Tree, Random Forest

• PCA

• Clustering: K-Means, Hierarchical, DBSCAN

6. Deep Learning with Python

• Neural Networks & Deep Learning

• Keras & TensorFlow

• ANN model building, activation functions

• CNN for image classification (Cats & Dogs Dataset)

• RNN, LSTM, GRU for sequence prediction

• Stock market prediction using LSTM

Who Can Join?

• Any graduate with basic computing knowledge

• Ideal for beginners, students, working professionals, career switchers, and aspiring data scientists