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Machine Learning

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Description

The topics covered in this course are:

- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

AED200.00

1 Week

Master AI with Lipslay’s Machine Learning course. Learn algorithms, data modeling, and AI techniques — all from home in Dubai.

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Frequently Asked Questions

It helps solve complex problems by analyzing large datasets. It powers innovations like personalized recommendations, autonomous vehicles, and medical diagnoses. It’s a key driver of AI advancements and automation.
ML works by using algorithms to find patterns in data, make predictions, and improve with experience. The process typically involves: Collecting data Preprocessing data Choosing an algorithm
Yes, basic programming skills are necessary, especially in languages like Python, which is widely used in Machine Learning.
Python: Most popular due to libraries like TensorFlow, PyTorch, and Scikit-learn. R: Often used for statistical analysis and data visualization. Java: Preferred in enterprise-level applications.
TensorFlow and PyTorch: For building and training ML models. Scikit-learn: For simpler ML tasks. Keras: For deep learning. Pandas and NumPy: For data manipulation and analysis. Matplotlib and Seaborn: For data visualization.
ML can be challenging for beginners due to its reliance on mathematics and programming. However, with structured learning and practice, it becomes manageable.
ML opens doors to roles such as: Machine Learning Engineer Data Scientist AI Specialist Research Scientist NLP Engineer
ML continues to grow, with applications expanding into fields like: Robotics and automation. Advanced healthcare systems. Climate change and environmental monitoring. Real-time language translation and AI-driven creativity.
Begin with the basics of Python and data manipulation using libraries like Pandas and NumPy. Learn fundamental ML concepts and algorithms. Practice building models using Scikit-learn, TensorFlow, or PyTorch. Work on real-world projects to gain hands-on experience.